query: I want a model to produce a prediction distribution closely approximating a delta function centered at a single value, but  I also need to make sure the optimal prediction distribution is not an actual delta function. If I use mean squared loss, the optimal distribution will tend to the delta function. So I need to address this issue by using a different loss. I am thinking about using generative models that can sample its output. Maybe rejection sampling? This problem might be related to mode collapses and how to avoid them, so I would like to explore that as well. In addition, the model should not use too many parameters and I want to keep everything in low dimensional spaces. Finally, to test if my model works, I want to test on a small image dataset. In this setting, the delta function would be the salient feature or event in each image.

positive:
Despite excellent progress in recent years, mode collapse remains a major
unsolved problem in generative adversarial networks (GANs).In this paper, we
present spectral regularization for GANs (SR-GANs), a new and robust method for
combating the mode collapse problem in GANs. Theoretical analysis shows that
the optimal solution to the discriminator has a strong relationship to the
spectral distributions of the weight matrix.Therefore, we monitor the spectral
distribution in the discriminator of spectral normalized GANs (SN-GANs), and
discover a phenomenon which we refer to as spectral collapse, where a large
number of singular values of the weight matrices drop dramatically when mode
collapse occurs. We show that there are strong evidence linking mode collapse
to spectral collapse; and based on this link, we set out to tackle spectral
collapse as a surrogate of mode collapse. We have developed a spectral
regularization method where we compensate the spectral distributions of the
weight matrices to prevent them from collapsing, which in turn successfully
prevents mode collapse in GANs. We provide theoretical explanations for why
SR-GANs are more stable and can provide better performances than SN-GANs. We
also present extensive experimental results and analysis to show that SR-GANs
not only always outperform SN-GANs but also always succeed in combating mode
collapse where SN-GANs fail. The code is available at
https://github.com/max-liu-112/SRGANs-Spectral-Regularization-GANs-.

positive:
Due to their flexibility and predictive performance, machine-learning based
regression methods have become an important tool for predictive modeling and
forecasting. However, most methods focus on estimating the conditional mean or
specific quantiles of the target quantity and do not provide the full
conditional distribution, which contains uncertainty information that might be
crucial for decision making. In this article, we provide a general solution by
transforming a conditional distribution estimation problem into a constrained
multi-class classification problem, in which tools such as deep neural
networks. We propose a novel joint binary cross-entropy loss function to
accomplish this goal. We demonstrate its performance in various simulation
studies comparing to state-of-the-art competing methods. Additionally, our
method shows improved accuracy in a probabilistic solar energy forecasting
problem.

positive:
Generative models have proven to be an outstanding tool for representing
high-dimensional probability distributions and generating realistic-looking
images. An essential characteristic of generative models is their ability to
produce multi-modal outputs. However, while training, they are often
susceptible to mode collapse, that is models are limited in mapping input noise
to only a few modes of the true data distribution. In this work, we draw
inspiration from Determinantal Point Process (DPP) to propose an unsupervised
penalty loss that alleviates mode collapse while producing higher quality
samples. DPP is an elegant probabilistic measure used to model negative
correlations within a subset and hence quantify its diversity. We use DPP
kernel to model the diversity in real data as well as in synthetic data. Then,
we devise an objective term that encourages generators to synthesize data with
similar diversity to real data. In contrast to previous state-of-the-art
generative models that tend to use additional trainable parameters or complex
training paradigms, our method does not change the original training scheme.
Embedded in an adversarial training and variational autoencoder, our Generative
DPP approach shows a consistent resistance to mode-collapse on a wide variety
of synthetic data and natural image datasets including MNIST, CIFAR10, and
CelebA, while outperforming state-of-the-art methods for data-efficiency,
generation quality, and convergence-time whereas being 5.8x faster than its
closest competitor.

positive:
Despite its success, generative adversarial networks (GANs) still suffer from
mode collapse, namely the generator can only map latent variables to a partial
set of modes of the target distribution. In this paper, we analyze and try to
regularize this issue with an independent and identically distributed (IID)
sampling perspective and emphasize that holding the IID property for generation
for target distribution (i.e. real distribution) can naturally avoid mode
collapse. This is based on the basic IID assumption for real data in machine
learning. However, though the source samples $\{\mathbf{z}\}$ obey IID, the
generations $\{G(\mathbf{z})\}$ may not necessarily be IID from the target
distribution. Based on this observation, we propose a necessary condition of
IID generation and provide a new loss to encourage the closeness between the
inverse source of real data and the Gaussian source in the latent space to
regularize the generation to be IID from the target distribution. The logic is
that the inverse samples from target data should also be IID in the source
distribution. Experiments on both synthetic and real-world data show the
effectiveness of our model.

positive:
In this paper, a shallow Ritz-type neural network for solving elliptic
equations with delta function singular sources on an interface is developed.
There are three novel features in the present work; namely, (i) the delta
function singularity is naturally removed, (ii) level set function is
introduced as a feature input, (iii) it is completely shallow, comprising only
one hidden layer. We first introduce the energy functional of the problem and
then transform the contribution of singular sources to a regular surface
integral along the interface. In such a way, the delta function singularity can
be naturally removed without introducing a discrete one that is commonly used
in traditional regularization methods, such as the well-known immersed boundary
method. The original problem is then reformulated as a minimization problem. We
propose a shallow Ritz-type neural network with one hidden layer to approximate
the global minimizer of the energy functional. As a result, the network is
trained by minimizing the loss function that is a discrete version of the
energy. In addition, we include the level set function of the interface as a
feature input of the network and find that it significantly improves the
training efficiency and accuracy. We perform a series of numerical tests to
show the accuracy of the present method and its capability for problems in
irregular domains and higher dimensions.

positive:
We present an approach for continual learning (CL) that is based on fully
probabilistic (or generative) models of machine learning. In contrast to, e.g.,
GANs that are "generative" in the sense that they can generate samples, fully
probabilistic models aim at modeling the data distribution directly.
Consequently, they provide functionalities that are highly relevant for
continual learning, such as density estimation (outlier detection) and sample
generation. As a concrete realization of generative continual learning, we
propose Gaussian Mixture Replay (GMR). GMR is a pseudo-rehearsal approach using
a Gaussian Mixture Model (GMM) instance for both generator and classifier
functionalities. Relying on the MNIST, FashionMNIST and Devanagari benchmarks,
we first demonstrate unsupervised task boundary detection by GMM density
estimation, which we also use to reject untypical generated samples. In
addition, we show that GMR is capable of class-conditional sampling in the way
of a cGAN. Lastly, we verify that GMR, despite its simple structure, achieves
state-of-the-art performance on common class-incremental learning problems at
very competitive time and memory complexity.

positive:
How to leverage cross-document interactions to improve ranking performance is
an important topic in information retrieval (IR) research. However, this topic
has not been well-studied in the learning-to-rank setting and most of the
existing work still treats each document independently while scoring. The
recent development of deep learning shows strength in modeling complex
relationships across sequences and sets. It thus motivates us to study how to
leverage cross-document interactions for learning-to-rank in the deep learning
framework. In this paper, we formally define the permutation-equivariance
requirement for a scoring function that captures cross-document interactions.
We then propose a self-attention based document interaction network and show
that it satisfies the permutation-equivariant requirement, and can generate
scores for document sets of varying sizes. Our proposed methods can
automatically learn to capture document interactions without any auxiliary
information, and can scale across large document sets. We conduct experiments
on three ranking datasets: the benchmark Web30k, a Gmail search, and a Google
Drive Quick Access dataset. Experimental results show that our proposed methods
are both more effective and efficient than baselines.

positive:
Accurate noise modelling is important for training of deep learning
reconstruction algorithms. While noise models are well known for traditional
imaging techniques, the noise distribution of a novel sensor may be difficult
to determine a priori. Therefore, we propose learning arbitrary noise
distributions. To do so, this paper proposes a fully connected neural network
model to map samples from a uniform distribution to samples of any explicitly
known probability density function. During the training, the Jensen-Shannon
divergence between the distribution of the model's output and the target
distribution is minimized. We experimentally demonstrate that our model
converges towards the desired state. It provides an alternative to existing
sampling methods such as inversion sampling, rejection sampling, Gaussian
mixture models and Markov-Chain-Monte-Carlo. Our model has high sampling
efficiency and is easily applied to any probability distribution, without the
need of further analytical or numerical calculations.

negative:
Many engineering problems require the prediction of
realization-to-realization variability or a refined description of modeled
quantities. In that case, it is necessary to sample elements from unknown
high-dimensional spaces with possibly millions of degrees of freedom. While
there exist methods able to sample elements from probability density functions
(PDF) with known shapes, several approximations need to be made when the
distribution is unknown. In this paper the sampling method, as well as the
inference of the underlying distribution, are both handled with a data-driven
method known as generative adversarial networks (GAN), which trains two
competing neural networks to produce a network that can effectively generate
samples from the training set distribution. In practice, it is often necessary
to draw samples from conditional distributions. When the conditional variables
are continuous, only one (if any) data point corresponding to a particular
value of a conditioning variable may be available, which is not sufficient to
estimate the conditional distribution. This work handles this problem using an
a priori estimation of the conditional moments of a PDF. Two approaches,
stochastic estimation, and an external neural network are compared here for
computing these moments; however, any preferred method can be used. The
algorithm is demonstrated in the case of the deconvolution of a filtered
turbulent flow field. It is shown that all the versions of the proposed
algorithm effectively sample the target conditional distribution with minimal
impact on the quality of the samples compared to state-of-the-art methods.
Additionally, the procedure can be used as a metric for the diversity of
samples generated by a conditional GAN (cGAN) conditioned with continuous
variables.

negative:
Extreme multi-label classification refers to supervised multi-label learning
involving hundreds of thousands or even millions of labels. Datasets in extreme
classification exhibit fit to power-law distribution, i.e. a large fraction of
labels have very few positive instances in the data distribution. Most
state-of-the-art approaches for extreme multi-label classification attempt to
capture correlation among labels by embedding the label matrix to a
low-dimensional linear sub-space. However, in the presence of power-law
distributed extremely large and diverse label spaces, structural assumptions
such as low rank can be easily violated.
  In this work, we present DiSMEC, which is a large-scale distributed framework
for learning one-versus-rest linear classifiers coupled with explicit capacity
control to control model size. Unlike most state-of-the-art methods, DiSMEC
does not make any low rank assumptions on the label matrix. Using double layer
of parallelization, DiSMEC can learn classifiers for datasets consisting
hundreds of thousands labels within few hours. The explicit capacity control
mechanism filters out spurious parameters which keep the model compact in size,
without losing prediction accuracy. We conduct extensive empirical evaluation
on publicly available real-world datasets consisting upto 670,000 labels. We
compare DiSMEC with recent state-of-the-art approaches, including - SLEEC which
is a leading approach for learning sparse local embeddings, and FastXML which
is a tree-based approach optimizing ranking based loss function. On some of the
datasets, DiSMEC can significantly boost prediction accuracies - 10% better
compared to SLECC and 15% better compared to FastXML, in absolute terms.

negative:
We focus on generative autoencoders, such as variational or adversarial
autoencoders, which jointly learn a generative model alongside an inference
model. Generative autoencoders are those which are trained to softly enforce a
prior on the latent distribution learned by the inference model. We call the
distribution to which the inference model maps observed samples, the learned
latent distribution, which may not be consistent with the prior. We formulate a
Markov chain Monte Carlo (MCMC) sampling process, equivalent to iteratively
decoding and encoding, which allows us to sample from the learned latent
distribution. Since, the generative model learns to map from the learned latent
distribution, rather than the prior, we may use MCMC to improve the quality of
samples drawn from the generative model, especially when the learned latent
distribution is far from the prior. Using MCMC sampling, we are able to reveal
previously unseen differences between generative autoencoders trained either
with or without a denoising criterion.

negative:
Standard objective functions used during the training of neural-network-based
predictive models do not consider clinical criteria, leading to models that are
not necessarily clinically acceptable. In this study, we look at this problem
from the perspective of the forecasting of future glucose values for diabetic
people. In this study, we propose the coherent mean squared glycemic error
(gcMSE) loss function. It penalizes the model during its training not only of
the prediction errors, but also on the predicted variation errors which is
important in glucose prediction. Moreover, it makes possible to adjust the
weighting of the different areas in the error space to better focus on
dangerous regions. In order to use the loss function in practice, we propose an
algorithm that progressively improves the clinical acceptability of the model,
so that we can achieve the best tradeoff possible between accuracy and given
clinical criteria. We evaluate the approaches using two diabetes datasets, one
having type-1 patients and the other type-2 patients. The results show that
using the gcMSE loss function, instead of a standard MSE loss function,
improves the clinical acceptability of the models. In particular, the
improvements are significant in the hypoglycemia region. We also show that this
increased clinical acceptability comes at the cost of a decrease in the average
accuracy of the model. Finally, we show that this tradeoff between accuracy and
clinical acceptability can be successfully addressed with the proposed
algorithm. For given clinical criteria, the algorithm can find the optimal
solution that maximizes the accuracy while at the same meeting the criteria.

negative:
The work investigates deep generative models, which allow us to use training
data from one domain to build a model for another domain. We propose the
Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint
distribution of objects from different domains. We extend the VBTAs objective
function by the relative constraints or triplets that sampled from the shared
latent space across domains. In other words, we combine the deep generative
models with a metric learning ideas in order to improve the final objective
with the triplets information. The performance of the VBTA model is
demonstrated on different tasks: image-to-image translation, bi-directional
image generation and cross-lingual document classification.

negative:
The training loss function that enforces certain training sample distribution
patterns plays a critical role in building a re-identification (ReID) system.
Besides the basic requirement of discrimination, i.e., the features
corresponding to different identities should not be mixed, additional
intra-class distribution constraints, such as features from the same identities
should be close to their centers, have been adopted to construct losses.
Despite the advances of various new loss functions, it is still challenging to
strike the balance between the need of reducing the intra-class variation and
allowing certain distribution freedom. In this paper, we propose a new loss
based on center predictivity, that is, a sample must be positioned in a
location of the feature space such that from it we can roughly predict the
location of the center of same-class samples. The prediction error is then
regarded as a loss called Center Prediction Loss (CPL). We show that, without
introducing additional hyper-parameters, this new loss leads to a more flexible
intra-class distribution constraint while ensuring the between-class samples
are well-separated. Extensive experiments on various real-world ReID datasets
show that the proposed loss can achieve superior performance and can also be
complementary to existing losses.

negative:
Given a task of predicting $Y$ from $X$, a loss function $L$, and a set of
probability distributions $\Gamma$ on $(X,Y)$, what is the optimal decision
rule minimizing the worst-case expected loss over $\Gamma$? In this paper, we
address this question by introducing a generalization of the principle of
maximum entropy. Applying this principle to sets of distributions with marginal
on $X$ constrained to be the empirical marginal from the data, we develop a
general minimax approach for supervised learning problems. While for some loss
functions such as squared-error and log loss, the minimax approach rederives
well-knwon regression models, for the 0-1 loss it results in a new linear
classifier which we call the maximum entropy machine. The maximum entropy
machine minimizes the worst-case 0-1 loss over the structured set of
distribution, and by our numerical experiments can outperform other well-known
linear classifiers such as SVM. We also prove a bound on the generalization
worst-case error in the minimax approach.

negative:
Complex deep learning models now achieve state of the art performance for
many document retrieval tasks. The best models process the query or claim
jointly with the document. However for fast scalable search it is desirable to
have document embeddings which are independent of the claim. In this paper we
show that knowledge distillation can be used to encourage a model that
generates claim independent document encodings to mimic the behavior of a more
complex model which generates claim dependent encodings. We explore this
approach in document retrieval for a fact extraction and verification task. We
show that by using the soft labels from a complex cross attention teacher
model, the performance of claim independent student LSTM or CNN models is
improved across all the ranking metrics. The student models we use are 12x
faster in runtime and 20x smaller in number of parameters than the teacher

negative:
Generative adversarial networks (GANs) are very popular to generate realistic
images, but they often suffer from the training instability issues and the
phenomenon of mode loss. In order to attain greater diversity in GAN
synthesized data, it is critical to solving the problem of mode loss. Our work
explores probabilistic approaches to GAN modelling that could allow us to
tackle these issues. We present Prb-GANs, a new variation that uses dropout to
create a distribution over the network parameters with the posterior learnt
using variational inference. We describe theoretically and validate
experimentally using simple and complex datasets the benefits of such an
approach. We look into further improvements using the concept of uncertainty
measures. Through a set of further modifications to the loss functions for each
network of the GAN, we are able to get results that show the improvement of GAN
performance. Our methods are extremely simple and require very little
modification to existing GAN architecture.

negative:
The choice of a loss function is an important factor when training neural
networks for image restoration problems, such as single image super resolution.
The loss function should encourage natural and perceptually pleasing results. A
popular choice for a loss is a pre-trained network, such as VGG, which is used
as a feature extractor for computing the difference between restored and
reference images. However, such an approach has multiple drawbacks: it is
computationally expensive, requires regularization and hyper-parameter tuning,
and involves a large network trained on an unrelated task. Furthermore, it has
been observed that there is no single loss function that works best across all
applications and across different datasets. In this work, we instead propose to
train a set of loss functions that are application specific in nature. Our loss
function comprises a series of discriminators that are trained to detect and
penalize the presence of application-specific artifacts. We show that a single
natural image and corresponding distortions are sufficient to train our feature
extractor that outperforms state-of-the-art loss functions in applications like
single image super resolution, denoising, and JPEG artifact removal. Finally,
we conclude that an effective loss function does not have to be a good
predictor of perceived image quality, but instead needs to be specialized in
identifying the distortions for a given restoration method.

negative:
Word embedding maps words into a low-dimensional continuous embedding space
by exploiting the local word collocation patterns in a small context window. On
the other hand, topic modeling maps documents onto a low-dimensional topic
space, by utilizing the global word collocation patterns in the same document.
These two types of patterns are complementary. In this paper, we propose a
generative topic embedding model to combine the two types of patterns. In our
model, topics are represented by embedding vectors, and are shared across
documents. The probability of each word is influenced by both its local context
and its topic. A variational inference method yields the topic embeddings as
well as the topic mixing proportions for each document. Jointly they represent
the document in a low-dimensional continuous space. In two document
classification tasks, our method performs better than eight existing methods,
with fewer features. In addition, we illustrate with an example that our method
can generate coherent topics even based on only one document.

negative:
Proposed methods for prediction interval estimation so far focus on cases
where input variables are numerical. In datasets with solely nominal input
variables, we observe records with the exact same input $x^u$, but different
real valued outputs due to the inherent noise in the system. Existing
prediction interval estimation methods do not use representations that can
accurately model such inherent noise in the case of nominal inputs. We propose
a new prediction interval estimation method tailored for this type of data,
which is prevalent in biology and medicine. We call this method Distribution
Adaptive Prediction Interval Estimation given Nominal inputs (DAPIEN) and has
four main phases. First, we select a distribution function that can best
represent the inherent noise of the system for all unique inputs. Then we infer
the parameters $\theta_i$ (e.g. $\theta_i=[mean_i, variance_i]$) of the
selected distribution function for all unique input vectors $x^u_i$ and
generate a new corresponding training set using pairs of $x^u_i, \theta_i$.
III). Then, we train a model to predict $\theta$ given a new $x_u$. Finally, we
calculate the prediction interval for a new sample using the inverse of the
cumulative distribution function once the parameters $\theta$ is predicted by
the trained model. We compared DAPIEN to the commonly used Bootstrap method on
three synthetic datasets. Our results show that DAPIEN provides tighter
prediction intervals while preserving the requested coverage when compared to
Bootstrap. This work can facilitate broader usage of regression methods in
medicine and biology where it is necessary to provide tight prediction
intervals while preserving coverage when input variables are nominal.

negative:
Large-scale pretrained language models have led to dramatic improvements in
text generation. Impressive performance can be achieved by finetuning only on a
small number of instances (few-shot setting). Nonetheless, almost all previous
work simply applies random sampling to select the few-shot training instances.
Little to no attention has been paid to the selection strategies and how they
would affect model performance. In this work, we present a study on training
instance selection in few-shot neural text generation. The selection decision
is made based only on the unlabeled data so as to identify the most worthwhile
data points that should be annotated under some budget of labeling cost. Based
on the intuition that the few-shot training instances should be diverse and
representative of the entire data distribution, we propose a simple selection
strategy with K-means clustering. We show that even with the naive
clustering-based approach, the generation models consistently outperform random
sampling on three text generation tasks: data-to-text generation, document
summarization and question generation. We hope that this work will call for
more attention on this largely unexplored area.

negative:
A basic requirement for a mathematical model is often that its solution
(output) shouldn't change much if the model's parameters (input) are perturbed.
This is important because the exact values of parameters may not be known and
one would like to avoid being mislead by an output obtained using incorrect
values. Thus, it's rarely enough to address an application by formulating a
model, solving the resulting optimization problem and presenting the solution
as the answer. One would need to confirm that the model is suitable, i.e.,
"good," and this can, at least in part, be achieved by considering a family of
optimization problems constructed by perturbing parameters of concern. The
resulting sensitivity analysis uncovers troubling situations with unstable
solutions, which we referred to as "bad" models, and indicates better model
formulations. Embedding an actual problem of interest within a family of
problems is also a primary path to optimality conditions as well as
computationally attractive, alternative problems, which under ideal
circumstances, and when properly tuned, may even furnish the minimum value of
the actual problem. The tuning of these alternative problems turns out to be
intimately tied to finding multipliers in optimality conditions and thus
emerges as a main component of several optimization algorithms. In fact, the
tuning amounts to solving certain dual optimization problems. In this tutorial,
we'll discuss the opportunities and insights afforded by this broad
perspective.

negative:
Neural networks are increasingly used to estimate parameters in quantitative
MRI, in particular in magnetic resonance fingerprinting. Their advantages over
the gold standard non-linear least square fitting are their superior speed and
their immunity to the non-convexity of many fitting problems. We find, however,
that in heterogeneous parameter spaces, i.e. in spaces in which the variance of
the estimated parameters varies considerably, good performance is hard to
achieve and requires arduous tweaking of the loss function, hyper parameters,
and the distribution of the training data in parameter space. Here, we address
these issues with a theoretically well-founded loss function: the Cram\'er-Rao
bound (CRB) provides a theoretical lower bound for the variance of an unbiased
estimator and we propose to normalize the squared error with respective CRB.
With this normalization, we balance the contributions of hard-to-estimate and
not-so-hard-to-estimate parameters and areas in parameter space, and avoid a
dominance of the former in the overall training loss. Further, the CRB-based
loss function equals one for a maximally-efficient unbiased estimator, which we
consider the ideal estimator. Hence, the proposed CRB-based loss function
provides an absolute evaluation metric. We compare a network trained with the
CRB-based loss with a network trained with the commonly used means squared
error loss and demonstrate the advantages of the former in numerical, phantom,
and in vivo experiments.

negative:
Traditional Relational Topic Models provide a way to discover the hidden
topics from a document network. Many theoretical and practical tasks, such as
dimensional reduction, document clustering, link prediction, benefit from this
revealed knowledge. However, existing relational topic models are based on an
assumption that the number of hidden topics is known in advance, and this is
impractical in many real-world applications. Therefore, in order to relax this
assumption, we propose a nonparametric relational topic model in this paper.
Instead of using fixed-dimensional probability distributions in its generative
model, we use stochastic processes. Specifically, a gamma process is assigned
to each document, which represents the topic interest of this document.
Although this method provides an elegant solution, it brings additional
challenges when mathematically modeling the inherent network structure of
typical document network, i.e., two spatially closer documents tend to have
more similar topics. Furthermore, we require that the topics are shared by all
the documents. In order to resolve these challenges, we use a subsampling
strategy to assign each document a different gamma process from the global
gamma process, and the subsampling probabilities of documents are assigned with
a Markov Random Field constraint that inherits the document network structure.
Through the designed posterior inference algorithm, we can discover the hidden
topics and its number simultaneously. Experimental results on both synthetic
and real-world network datasets demonstrate the capabilities of learning the
hidden topics and, more importantly, the number of topics.

negative:
Despite their empirical success, most existing listwiselearning-to-rank (LTR)
models are not built to be robust to errors in labeling or annotation,
distributional data shift, or adversarial data perturbations. To fill this gap,
we introduce a new listwise LTR model called Distributionally Robust
Multi-output Regression Ranking (DRMRR). Different from existing methods, the
scoring function of DRMRR was designed as a multivariate mapping from a feature
vector to a vector of deviation scores, which captures local context
information and cross-document interactions. DRMRR uses a Distributionally
Robust Optimization (DRO) framework to minimize a multi-output loss function
under the most adverse distributions in the neighborhood of the empirical data
distribution defined by a Wasserstein ball. We show that this is equivalent to
a regularized regression problem with a matrix norm regularizer. Our
experiments were conducted on two real-world applications, medical document
retrieval, and drug response prediction, showing that DRMRR notably outperforms
state-of-the-art LTR models. We also conducted a comprehensive analysis to
assess the resilience of DRMRR against various types of noise: Gaussian noise,
adversarial perturbations, and label poisoning. We show that DRMRR is not only
able to achieve significantly better performance than other baselines, but it
can maintain a relatively stable performance as more noise is added to the
data.

negative:
Generative adversarial networks (GANs) are innovative techniques for learning
generative models of complex data distributions from samples. Despite
remarkable recent improvements in generating realistic images, one of their
major shortcomings is the fact that in practice, they tend to produce samples
with little diversity, even when trained on diverse datasets. This phenomenon,
known as mode collapse, has been the main focus of several recent advances in
GANs. Yet there is little understanding of why mode collapse happens and why
existing approaches are able to mitigate mode collapse. We propose a principled
approach to handling mode collapse, which we call packing. The main idea is to
modify the discriminator to make decisions based on multiple samples from the
same class, either real or artificially generated. We borrow analysis tools
from binary hypothesis testing---in particular the seminal result of Blackwell
[Bla53]---to prove a fundamental connection between packing and mode collapse.
We show that packing naturally penalizes generators with mode collapse, thereby
favoring generator distributions with less mode collapse during the training
process. Numerical experiments on benchmark datasets suggests that packing
provides significant improvements in practice as well.

negative:
In clinical trials and other applications, we often see regions of the
feature space that appear to exhibit interesting behaviour, but it is unclear
whether these observed phenomena are reflected at the population level.
Focusing on a regression setting, we consider the subgroup selection challenge
of identifying a region of the feature space on which the regression function
exceeds a pre-determined threshold. We formulate the problem as one of
constrained optimisation, where we seek a low-complexity, data-dependent
selection set on which, with a guaranteed probability, the regression function
is uniformly at least as large as the threshold; subject to this constraint, we
would like the region to contain as much mass under the marginal feature
distribution as possible. This leads to a natural notion of regret, and our
main contribution is to determine the minimax optimal rate for this regret in
both the sample size and the Type I error probability. The rate involves a
delicate interplay between parameters that control the smoothness of the
regression function, as well as exponents that quantify the extent to which the
optimal selection set at the population level can be approximated by families
of well-behaved subsets. Finally, we expand the scope of our previous results
by illustrating how they may be generalised to a treatment and control setting,
where interest lies in the heterogeneous treatment effect.

negative:
Latent topic models have been successfully applied as an unsupervised topic
discovery technique in large document collections. With the proliferation of
hypertext document collection such as the Internet, there has also been great
interest in extending these approaches to hypertext [6, 9]. These approaches
typically model links in an analogous fashion to how they model words - the
document-link co-occurrence matrix is modeled in the same way that the
document-word co-occurrence matrix is modeled in standard topic models. In this
paper we present a probabilistic generative model for hypertext document
collections that explicitly models the generation of links. Specifically, links
from a word w to a document d depend directly on how frequent the topic of w is
in d, in addition to the in-degree of d. We show how to perform EM learning on
this model efficiently. By not modeling links as analogous to words, we end up
using far fewer free parameters and obtain better link prediction results.

negative:
Recently, the computer vision and machine learning community has been in
favor of feature extraction pipelines that rely on a coding step followed by a
linear classifier, due to their overall simplicity, well understood properties
of linear classifiers, and their computational efficiency. In this paper we
propose a novel view of this pipeline based on kernel methods and Nystrom
sampling. In particular, we focus on the coding of a data point with a local
representation based on a dictionary with fewer elements than the number of
data points, and view it as an approximation to the actual function that would
compute pair-wise similarity to all data points (often too many to compute in
practice), followed by a Nystrom sampling step to select a subset of all data
points.
  Furthermore, since bounds are known on the approximation power of Nystrom
sampling as a function of how many samples (i.e. dictionary size) we consider,
we can derive bounds on the approximation of the exact (but expensive to
compute) kernel matrix, and use it as a proxy to predict accuracy as a function
of the dictionary size, which has been observed to increase but also to
saturate as we increase its size. This model may help explaining the positive
effect of the codebook size and justifying the need to stack more layers (often
referred to as deep learning), as flat models empirically saturate as we add
more complexity.

negative:
At the present time, sequential item recommendation models are compared by
calculating metrics on a small item subset (target set) to speed up
computation. The target set contains the relevant item and a set of negative
items that are sampled from the full item set. Two well-known strategies to
sample negative items are uniform random sampling and sampling by popularity to
better approximate the item frequency distribution in the dataset. Most
recently published papers on sequential item recommendation rely on sampling by
popularity to compare the evaluated models. However, recent work has already
shown that an evaluation with uniform random sampling may not be consistent
with the full ranking, that is, the model ranking obtained by evaluating a
metric using the full item set as target set, which raises the question whether
the ranking obtained by sampling by popularity is equal to the full ranking. In
this work, we re-evaluate current state-of-the-art sequential recommender
models from the point of view, whether these sampling strategies have an impact
on the final ranking of the models. We therefore train four recently proposed
sequential recommendation models on five widely known datasets. For each
dataset and model, we employ three evaluation strategies. First, we compute the
full model ranking. Then we evaluate all models on a target set sampled by the
two different sampling strategies, uniform random sampling and sampling by
popularity with the commonly used target set size of 100, compute the model
ranking for each strategy and compare them with each other. Additionally, we
vary the size of the sampled target set. Overall, we find that both sampling
strategies can produce inconsistent rankings compared with the full ranking of
the models. Furthermore, both sampling by popularity and uniform random
sampling do not consistently produce the same ranking ...

negative:
A learned generative model often produces biased statistics relative to the
underlying data distribution. A standard technique to correct this bias is
importance sampling, where samples from the model are weighted by the
likelihood ratio under model and true distributions. When the likelihood ratio
is unknown, it can be estimated by training a probabilistic classifier to
distinguish samples from the two distributions. We employ this likelihood-free
importance weighting method to correct for the bias in generative models. We
find that this technique consistently improves standard goodness-of-fit metrics
for evaluating the sample quality of state-of-the-art deep generative models,
suggesting reduced bias. Finally, we demonstrate its utility on representative
applications in a) data augmentation for classification using generative
adversarial networks, and b) model-based policy evaluation using off-policy
data.

negative:
In most machine learning tasks, we evaluate a model $M$ on a given data
population $S$ by measuring a population-level metric $F(S;M)$. Examples of
such evaluation metric $F$ include precision/recall for (binary) recognition,
the F1 score for multi-class classification, and the BLEU metric for language
generation. On the other hand, the model $M$ is trained by optimizing a
sample-level loss $G(S_t;M)$ at each learning step $t$, where $S_t$ is a subset
of $S$ (a.k.a. the mini-batch). Popular choices of $G$ include cross-entropy
loss, the Dice loss, and sentence-level BLEU scores. A fundamental assumption
behind this paradigm is that the mean value of the sample-level loss $G$, if
averaged over all possible samples, should effectively represent the
population-level metric $F$ of the task, such as, that $\mathbb{E}[ G(S_t;M) ]
\approx F(S;M)$.
  In this paper, we systematically investigate the above assumption in several
NLP tasks. We show, both theoretically and experimentally, that some popular
designs of the sample-level loss $G$ may be inconsistent with the true
population-level metric $F$ of the task, so that models trained to optimize the
former can be substantially sub-optimal to the latter, a phenomenon we call it,
Simpson's bias, due to its deep connections with the classic paradox known as
Simpson's reversal paradox in statistics and social sciences.

negative:
Generative adversarial networks (GANs) provide state-of-the-art results in
image generation. However, despite being so powerful, they still remain very
challenging to train. This is in particular caused by their highly non-convex
optimization space leading to a number of instabilities. Among them, mode
collapse stands out as one of the most daunting ones. This undesirable event
occurs when the model can only fit a few modes of the data distribution, while
ignoring the majority of them. In this work, we combat mode collapse using
second-order gradient information. To do so, we analyse the loss surface
through its Hessian eigenvalues, and show that mode collapse is related to the
convergence towards sharp minima. In particular, we observe how the eigenvalues
of the $G$ are directly correlated with the occurrence of mode collapse.
Finally, motivated by these findings, we design a new optimization algorithm
called nudged-Adam (NuGAN) that uses spectral information to overcome mode
collapse, leading to empirically more stable convergence properties.

negative:
Due to the increasing amount of data on the internet, finding a
highly-informative, low-dimensional representation for text is one of the main
challenges for efficient natural language processing tasks including text
classification. This representation should capture the semantic information of
the text while retaining their relevance level for document classification.
This approach maps the documents with similar topics to a similar space in
vector space representation. To obtain representation for large text, we
propose the utilization of deep Siamese neural networks. To embed document
relevance in topics in the distributed representation, we use a Siamese neural
network to jointly learn document representations. Our Siamese network consists
of two sub-network of multi-layer perceptron. We examine our representation for
the text categorization task on BBC news dataset. The results show that the
proposed representations outperform the conventional and state-of-the-art
representations in the text classification task on this dataset.

negative:
We analyse optimum reject strategies for prototype-based classifiers and
real-valued rejection measures, using the distance of a data point to the
closest prototype or probabilistic counterparts. We compare reject schemes with
global thresholds, and local thresholds for the Voronoi cells of the
classifier. For the latter, we develop a polynomial-time algorithm to compute
optimum thresholds based on a dynamic programming scheme, and we propose an
intuitive linear time, memory efficient approximation thereof with competitive
accuracy. Evaluating the performance in various benchmarks, we conclude that
local reject options are beneficial in particular for simple prototype-based
classifiers, while the improvement is less pronounced for advanced models. For
the latter, an accuracy-reject curve which is comparable to support vector
machine classifiers with state of the art reject options can be reached.

negative:
Document network embedding aims at learning representations for a structured
text corpus i.e. when documents are linked to each other. Recent algorithms
extend network embedding approaches by incorporating the text content
associated with the nodes in their formulations. In most cases, it is hard to
interpret the learned representations. Moreover, little importance is given to
the generalization to new documents that are not observed within the network.
In this paper, we propose an interpretable and inductive document network
embedding method. We introduce a novel mechanism, the Topic-Word Attention
(TWA), that generates document representations based on the interplay between
word and topic representations. We train these word and topic vectors through
our general model, Inductive Document Network Embedding (IDNE), by leveraging
the connections in the document network. Quantitative evaluations show that our
approach achieves state-of-the-art performance on various networks and we
qualitatively show that our model produces meaningful and interpretable
representations of the words, topics and documents.

negative:
This paper introduces an alternative approach to sampling from autoregressive
models. Autoregressive models are typically sampled sequentially, according to
the transition dynamics defined by the model. Instead, we propose a sampling
procedure that initializes a sequence with white noise and follows a Markov
chain defined by Langevin dynamics on the global log-likelihood of the
sequence. This approach parallelizes the sampling process and generalizes to
conditional sampling. Using an autoregressive model as a Bayesian prior, we can
steer the output of a generative model using a conditional likelihood or
constraints. We apply these techniques to autoregressive models in the visual
and audio domains, with competitive results for audio source separation,
super-resolution, and inpainting.

negative:
In this paper we study the problem of learning from multiple modal data for
purpose of document classification. In this problem, each document is composed
two different modals of data, i.e., an image and a text. Cross-modal factor
analysis (CFA) has been proposed to project the two different modals of data to
a shared data space, so that the classification of a image or a text can be
performed directly in this space. A disadvantage of CFA is that it has ignored
the supervision information. In this paper, we improve CFA by incorporating the
supervision information to represent and classify both image and text modals of
documents. We project both image and text data to a shared data space by factor
analysis, and then train a class label predictor in the shared space to use the
class label information. The factor analysis parameter and the predictor
parameter are learned jointly by solving one single objective function. With
this objective function, we minimize the distance between the projections of
image and text of the same document, and the classification error of the
projection measured by hinge loss function. The objective function is optimized
by an alternate optimization strategy in an iterative algorithm. Experiments in
two different multiple modal document data sets show the advantage of the
proposed algorithm over other CFA methods.

negative:
The predictive quality of machine learning models is typically measured in
terms of their (approximate) expected prediction accuracy or the so-called Area
Under the Curve (AUC). Minimizing the reciprocals of these measures are the
goals of supervised learning. However, when the models are constructed by the
means of empirical risk minimization (ERM), surrogate functions such as the
logistic loss or hinge loss are optimized instead. In this work, we show that
in the case of linear predictors, the expected error and the expected ranking
loss can be effectively approximated by smooth functions whose closed form
expressions and those of their first (and second) order derivatives depend on
the first and second moments of the data distribution, which can be
precomputed. Hence, the complexity of an optimization algorithm applied to
these functions does not depend on the size of the training data. These
approximation functions are derived under the assumption that the output of the
linear classifier for a given data set has an approximately normal
distribution. We argue that this assumption is significantly weaker than the
Gaussian assumption on the data itself and we support this claim by
demonstrating that our new approximation is quite accurate on data sets that
are not necessarily Gaussian. We present computational results that show that
our proposed approximations and related optimization algorithms can produce
linear classifiers with similar or better test accuracy or AUC, than those
obtained using state-of-the-art approaches, in a fraction of the time.

negative:
BERT based ranking models have achieved superior performance on various
information retrieval tasks. However, the large number of parameters and
complex self-attention operation come at a significant latency overhead. To
remedy this, recent works propose late-interaction architectures, which allow
pre-computation of intermediate document representations, thus reducing the
runtime latency. Nonetheless, having solved the immediate latency issue, these
methods now introduce storage costs and network fetching latency, which limits
their adoption in real-life production systems.
  In this work, we propose the Succinct Document Representation (SDR) scheme
that computes highly compressed intermediate document representations,
mitigating the storage/network issue. Our approach first reduces the dimension
of token representations by encoding them using a novel autoencoder
architecture that uses the document's textual content in both the encoding and
decoding phases. After this token encoding step, we further reduce the size of
entire document representations using a modern quantization technique.
  Extensive evaluations on passage re-reranking on the MSMARCO dataset show
that compared to existing approaches using compressed document representations,
our method is highly efficient, achieving 4x-11.6x better compression rates for
the same ranking quality.

negative:
We consider local planning in fixed-horizon MDPs with a generative model
under the assumption that the optimal value function lies close to the span of
a feature map. The generative model provides a local access to the MDP: The
planner can ask for random transitions from previously returned states and
arbitrary actions, and features are only accessible for states that are
encountered in this process. As opposed to previous work (e.g. Lattimore et al.
(2020)) where linear realizability of all policies was assumed, we consider the
significantly relaxed assumption of a single linearly realizable
(deterministic) policy. A recent lower bound by Weisz et al. (2020) established
that the related problem when the action-value function of the optimal policy
is linearly realizable requires an exponential number of queries, either in $H$
(the horizon of the MDP) or $d$ (the dimension of the feature mapping). Their
construction crucially relies on having an exponentially large action set. In
contrast, in this work, we establish that poly$(H,d)$ planning is possible with
state value function realizability whenever the action set has a constant size.
In particular, we present the TensorPlan algorithm which uses
poly$((dH/\delta)^A)$ simulator queries to find a $\delta$-optimal policy
relative to any deterministic policy for which the value function is linearly
realizable with some bounded parameter. This is the first algorithm to give a
polynomial query complexity guarantee using only linear-realizability of a
single competing value function. Whether the computation cost is similarly
bounded remains an open question. We extend the upper bound to the
near-realizable case and to the infinite-horizon discounted setup. We also
present a lower bound in the infinite-horizon episodic setting: Planners that
achieve constant suboptimality need exponentially many queries, either in $d$
or the number of actions.

negative:
In neural networks, the loss function represents the core of the learning
process that leads the optimizer to an approximation of the optimal convergence
error. Convolutional neural networks (CNN) use the loss function as a
supervisory signal to train a deep model and contribute significantly to
achieving the state of the art in some fields of artificial vision.
Cross-entropy and Center loss functions are commonly used to increase the
discriminating power of learned functions and increase the generalization
performance of the model. Center loss minimizes the class intra-class variance
and at the same time penalizes the long distance between the deep features
inside each class. However, the total error of the center loss will be heavily
influenced by the majority of the instances and can lead to a freezing state in
terms of intra-class variance. To address this, we introduce a new loss
function called sigma squared reduction loss ($\sigma^2$R loss), which is
regulated by a sigmoid function to inflate/deflate the error per instance and
then continue to reduce the intra-class variance. Our loss has clear intuition
and geometric interpretation, furthermore, we demonstrate by experiments the
effectiveness of our proposal on several benchmark datasets showing the
intra-class variance reduction and overcoming the results obtained with center
loss and soft nearest neighbour functions.

negative:
Several interesting generative learning algorithms involve a complex
probability distribution over many random variables, involving intractable
normalization constants or latent variable normalization. Some of them may even
not have an analytic expression for the unnormalized probability function and
no tractable approximation. This makes it difficult to estimate the quality of
these models, once they have been trained, or to monitor their quality (e.g.
for early stopping) while training. A previously proposed method is based on
constructing a non-parametric density estimator of the model's probability
function from samples generated by the model. We revisit this idea, propose a
more efficient estimator, and prove that it provides a lower bound on the true
test log-likelihood, and an unbiased estimator as the number of generated
samples goes to infinity, although one that incorporates the effect of poor
mixing. We further propose a biased variant of the estimator that can be used
reliably with a finite number of samples for the purpose of model comparison.

negative:
Training supervised image synthesis models requires a critic to compare two
images: the ground truth to the result. Yet, this basic functionality remains
an open problem. A popular line of approaches uses the L1 (mean absolute error)
loss, either in the pixel or the feature space of pretrained deep networks.
However, we observe that these losses tend to produce overly blurry and grey
images, and other techniques such as GANs need to be employed to fight these
artifacts. In this work, we introduce an information theory based approach to
measuring similarity between two images. We argue that a good reconstruction
should have high mutual information with the ground truth. This view enables
learning a lightweight critic to "calibrate" a feature space in a contrastive
manner, such that reconstructions of corresponding spatial patches are brought
together, while other patches are repulsed. We show that our formulation
immediately boosts the perceptual realism of output images when used as a
drop-in replacement for the L1 loss, with or without an additional GAN loss.

negative:
One of the major prerequisites for any deep learning approach is the
availability of large-scale training data. When dealing with scanned document
images in real world scenarios, the principal information of its content is
stored in the layout itself. In this work, we have proposed an automated deep
generative model using Graph Neural Networks (GNNs) to generate synthetic data
with highly variable and plausible document layouts that can be used to train
document interpretation systems, in this case, specially in digital mailroom
applications. It is also the first graph-based approach for document layout
generation task experimented on administrative document images, in this case,
invoices.

negative:
Document classification is a challenging task with important applications.
The deep learning approaches to the problem have gained much attention
recently. Despite the progress, the proposed models do not incorporate the
knowledge of the document structure in the architecture efficiently and not
take into account the contexting importance of words and sentences. In this
paper, we propose a new approach based on a combination of convolutional neural
networks, gated recurrent units, and attention mechanisms for document
classification tasks. The main contribution of this work is the use of
convolution layers to extract more meaningful, generalizable and abstract
features by the hierarchical representation. The proposed method in this paper
improves the results of the current attention-based approaches for document
classification.

negative:
The Huber loss is a robust loss function used for a wide range of regression
tasks. To utilize the Huber loss, a parameter that controls the transitions
from a quadratic function to an absolute value function needs to be selected.
We believe the standard probabilistic interpretation that relates the Huber
loss to the Huber density fails to provide adequate intuition for identifying
the transition point. As a result, a hyper-parameter search is often necessary
to determine an appropriate value. In this work, we propose an alternative
probabilistic interpretation of the Huber loss, which relates minimizing the
loss to minimizing an upper-bound on the Kullback-Leibler divergence between
Laplace distributions, where one distribution represents the noise in the
ground-truth and the other represents the noise in the prediction. In addition,
we show that the parameters of the Laplace distributions are directly related
to the transition point of the Huber loss. We demonstrate, through a toy
problem, that the optimal transition point of the Huber loss is closely related
to the distribution of the noise in the ground-truth data. As a result, our
interpretation provides an intuitive way to identify well-suited
hyper-parameters by approximating the amount of noise in the data, which we
demonstrate through a case study and experimentation on the Faster R-CNN and
RetinaNet object detectors.

negative:
We look at a specific aspect of model interpretability: models often need to
be constrained in size for them to be considered interpretable. But smaller
models also tend to have high bias. This suggests a trade-off between
interpretability and accuracy. Our work addresses this by: (a) showing that
learning a training distribution (often different from the test distribution)
can often increase accuracy of small models, and therefore may be used as a
strategy to compensate for small sizes, and (b) providing a model-agnostic
algorithm to learn such training distributions. We pose the distribution
learning problem as one of optimizing parameters for an Infinite Beta Mixture
Model based on a Dirichlet Process, so that the held-out accuracy of a model
trained on a sample from this distribution is maximized. To make computation
tractable, we project the training data onto one dimension: prediction
uncertainty scores as provided by a highly accurate oracle model. A Bayesian
Optimizer is used for learning the parameters. Empirical results using multiple
real world datasets, various oracles and interpretable models with different
notions of model sizes, are presented. We observe significant relative
improvements in the F1-score in most cases, occasionally seeing improvements
greater than 100% over baselines. Additionally we show that the proposed
algorithm provides the following benefits: (a) its a framework which allows for
flexibility in implementation, (b) it can be used across feature spaces, e.g.,
the text classification accuracy of a Decision Tree using character n-grams is
shown to improve when using a Gated Recurrent Unit as an oracle, which uses a
sequence of characters as its input, (c) it can be used to train models that
have a non-differentiable training loss, e.g., Decision Trees, and (d)
reasonable defaults exist for most parameters of the algorithm, which makes it
convenient to use.

negative:
In a regression setting we propose algorithms that reduce the dimensionality
of the features while simultaneously maximizing a statistical measure of
dependence known as distance correlation between the low-dimensional features
and a response variable. This helps in solving the prediction problem with a
low-dimensional set of features. Our setting is different from subset-selection
algorithms where the problem is to choose the best subset of features for
regression. Instead, we attempt to generate a new set of low-dimensional
features as in a feature-learning setting. We attempt to keep our proposed
approach as model-free and our algorithm does not assume the application of any
specific regression model in conjunction with the low-dimensional features that
it learns. The algorithm is iterative and is fomulated as a combination of the
majorization-minimization and concave-convex optimization procedures. We also
present spectral radius based convergence results for the proposed iterations.

negative:
Causal machine-learning is about predicting the net-effect (true-lift) of
treatments. Given the data of a treatment group and a control group, it is
similar to a standard supervised-learning problem. Unfortunately, there is no
similarly well-defined loss function due to the lack of point-wise true values
in the data. Many advances in modern machine-learning are not directly
applicable due to the absence of such loss function.
  We propose a novel method to define a loss function in this context, which is
equal to mean-square-error (MSE) in a standard regression problem. Our loss
function is universally applicable, thus providing a general standard to
evaluate the quality of any model/strategy that predicts the true-lift. We
demonstrate that despite its novel definition, one can still perform gradient
descent directly on this loss function to find the best fit. This leads to a
new way to train any parameter-based model, such as deep neural networks, to
solve causal machine-learning problems without going through the meta-learner
strategy.

negative:
Set prediction is about learning to predict a collection of unordered
variables with unknown interrelations. Training such models with set losses
imposes the structure of a metric space over sets. We focus on stochastic and
underdefined cases, where an incorrectly chosen loss function leads to
implausible predictions. Example tasks include conditional point-cloud
reconstruction and predicting future states of molecules. In this paper, we
propose an alternative to training via set losses by viewing learning as
conditional density estimation. Our learning framework fits deep energy-based
models and approximates the intractable likelihood with gradient-guided
sampling. Furthermore, we propose a stochastically augmented prediction
algorithm that enables multiple predictions, reflecting the possible variations
in the target set. We empirically demonstrate on a variety of datasets the
capability to learn multi-modal densities and produce different plausible
predictions. Our approach is competitive with previous set prediction models on
standard benchmarks. More importantly, it extends the family of addressable
tasks beyond those that have unambiguous predictions.

negative:
The Maximum Mean Discrepancy (MMD) has been the state-of-the-art
nonparametric test for tackling the two-sample problem. Its statistic is given
by the difference in expectations of the witness function, a real-valued
function defined as a weighted sum of kernel evaluations on a set of basis
points. Typically the kernel is optimized on a training set, and hypothesis
testing is performed on a separate test set to avoid overfitting (i.e., control
type-I error). That is, the test set is used to simultaneously estimate the
expectations and define the basis points, while the training set only serves to
select the kernel and is discarded. In this work, we propose to use the
training data to also define the weights and the basis points for better data
efficiency. We show that 1) the new test is consistent and has a
well-controlled type-I error; 2) the optimal witness function is given by a
precision-weighted mean in the reproducing kernel Hilbert space associated with
the kernel; and 3) the test power of the proposed test is comparable or exceeds
that of the MMD and other modern tests, as verified empirically on challenging
synthetic and real problems (e.g., Higgs data).

negative:
Deep learning has been immensely successful at a variety of tasks, ranging
from classification to AI. Learning corresponds to fitting training data, which
is implemented by descending a very high-dimensional loss function.
Understanding under which conditions neural networks do not get stuck in poor
minima of the loss, and how the landscape of that loss evolves as depth is
increased remains a challenge. Here we predict, and test empirically, an
analogy between this landscape and the energy landscape of repulsive ellipses.
We argue that in FC networks a phase transition delimits the over- and
under-parametrized regimes where fitting can or cannot be achieved. In the
vicinity of this transition, properties of the curvature of the minima of the
loss are critical. This transition shares direct similarities with the jamming
transition by which particles form a disordered solid as the density is
increased, which also occurs in certain classes of computational optimization
and learning problems such as the perceptron. Our analysis gives a simple
explanation as to why poor minima of the loss cannot be encountered in the
overparametrized regime, and puts forward the surprising result that the
ability of fully connected networks to fit random data is independent of their
depth. Our observations suggests that this independence also holds for real
data. We also study a quantity $\Delta$ which characterizes how well
($\Delta<0$) or badly ($\Delta>0$) a datum is learned. At the critical point it
is power-law distributed, $P_+(\Delta)\sim\Delta^\theta$ for $\Delta>0$ and
$P_-(\Delta)\sim(-\Delta)^{-\gamma}$ for $\Delta<0$, with $\theta\approx0.3$
and $\gamma\approx0.2$. This observation suggests that near the transition the
loss landscape has a hierarchical structure and that the learning dynamics is
prone to avalanche-like dynamics, with abrupt changes in the set of patterns
that are learned.

negative:
Recent methods for knowledge grounded dialogs generate responses by
incorporating information from an external textual document. These methods do
not require the exact document to be known during training and rely on the use
of a retrieval system to fetch relevant documents from a large index. The
documents used to generate the responses are modeled as latent variables whose
prior probabilities need to be estimated. Models such as RAG and REALM,
marginalize the document probabilities over the documents retrieved from the
index to define the log likelihood loss function which is optimized end-to-end.
  In this paper, we develop a variational approach to the above technique
wherein, we instead maximize the Evidence Lower bound (ELBO). Using a
collection of three publicly available open-conversation datasets, we
demonstrate how the posterior distribution, that has information from the
ground-truth response, allows for a better approximation of the objective
function during training. To overcome the challenges associated with sampling
over a large knowledge collection, we develop an efficient approach to
approximate the ELBO. To the best of our knowledge we are the first to apply
variational training for open-scale unsupervised knowledge grounded dialog
systems.

negative:
A common assumption in causal modeling posits that the data is generated by a
set of independent mechanisms, and algorithms should aim to recover this
structure. Standard unsupervised learning, however, is often concerned with
training a single model to capture the overall distribution or aspects thereof.
Inspired by clustering approaches, we consider mixtures of implicit generative
models that ``disentangle'' the independent generative mechanisms underlying
the data. Relying on an additional set of discriminators, we propose a
competitive training procedure in which the models only need to capture the
portion of the data distribution from which they can produce realistic samples.
As a by-product, each model is simpler and faster to train. We empirically show
that our approach splits the training distribution in a sensible way and
increases the quality of the generated samples.

negative:
This work focuses on combining nonparametric topic models with Auto-Encoding
Variational Bayes (AEVB). Specifically, we first propose iTM-VAE, where the
topics are treated as trainable parameters and the document-specific topic
proportions are obtained by a stick-breaking construction. The inference of
iTM-VAE is modeled by neural networks such that it can be computed in a simple
feed-forward manner. We also describe how to introduce a hyper-prior into
iTM-VAE so as to model the uncertainty of the prior parameter. Actually, the
hyper-prior technique is quite general and we show that it can be applied to
other AEVB based models to alleviate the {\it collapse-to-prior} problem
elegantly. Moreover, we also propose HiTM-VAE, where the document-specific
topic distributions are generated in a hierarchical manner. HiTM-VAE is even
more flexible and can generate topic distributions with better variability.
Experimental results on 20News and Reuters RCV1-V2 datasets show that the
proposed models outperform the state-of-the-art baselines significantly. The
advantages of the hyper-prior technique and the hierarchical model construction
are also confirmed by experiments.

negative:
In this work we study loss functions for learning and evaluating probability
distributions over large discrete domains. Unlike classification or regression
where a wide variety of loss functions are used, in the distribution learning
and density estimation literature, very few losses outside the dominant $log\
loss$ are applied. We aim to understand this fact, taking an axiomatic approach
to the design of loss functions for learning distributions. We start by
proposing a set of desirable criteria that any good loss function should
satisfy. Intuitively, these criteria require that the loss function faithfully
evaluates a candidate distribution, both in expectation and when estimated on a
few samples. Interestingly, we observe that \emph{no loss function} possesses
all of these criteria. However, one can circumvent this issue by introducing a
natural restriction on the set of candidate distributions. Specifically, we
require that candidates are $calibrated$ with respect to the target
distribution, i.e., they may contain less information than the target but
otherwise do not significantly distort the truth. We show that, after
restricting to this set of distributions, the log loss, along with a large
variety of other losses satisfy the desired criteria. These results pave the
way for future investigations of distribution learning that look beyond the log
loss, choosing a loss function based on application or domain need.

negative:
Developments in deep generative models have allowed for tractable learning of
high-dimensional data distributions. While the employed learning procedures
typically assume that training data is drawn i.i.d. from the distribution of
interest, it may be desirable to model distinct distributions which are
observed sequentially, such as when different classes are encountered over
time. Although conditional variations of deep generative models permit multiple
distributions to be modeled by a single network in a disentangled fashion, they
are susceptible to catastrophic forgetting when the distributions are
encountered sequentially. In this paper, we adapt recent work in reducing
catastrophic forgetting to the task of training generative adversarial networks
on a sequence of distinct distributions, enabling continual generative
modeling.

negative:
In linear regression we wish to estimate the optimum linear least squares
predictor for a distribution over $d$-dimensional input points and real-valued
responses, based on a small sample. Under standard random design analysis,
where the sample is drawn i.i.d. from the input distribution, the least squares
solution for that sample can be viewed as the natural estimator of the optimum.
Unfortunately, this estimator almost always incurs an undesirable bias coming
from the randomness of the input points, which is a significant bottleneck in
model averaging. In this paper we show that it is possible to draw a non-i.i.d.
sample of input points such that, regardless of the response model, the least
squares solution is an unbiased estimator of the optimum. Moreover, this sample
can be produced efficiently by augmenting a previously drawn i.i.d. sample with
an additional set of $d$ points, drawn jointly according to a certain
determinantal point process constructed from the input distribution rescaled by
the squared volume spanned by the points. Motivated by this, we develop a
theoretical framework for studying volume-rescaled sampling, and in the process
prove a number of new matrix expectation identities. We use them to show that
for any input distribution and $\epsilon>0$ there is a random design consisting
of $O(d\log d+ d/\epsilon)$ points from which an unbiased estimator can be
constructed whose expected square loss over the entire distribution is bounded
by $1+\epsilon$ times the loss of the optimum. We provide efficient algorithms
for generating such unbiased estimators in a number of practical settings and
support our claims experimentally.

negative:
This paper presents a new state-of-the-art for document image classification
and retrieval, using features learned by deep convolutional neural networks
(CNNs). In object and scene analysis, deep neural nets are capable of learning
a hierarchical chain of abstraction from pixel inputs to concise and
descriptive representations. The current work explores this capacity in the
realm of document analysis, and confirms that this representation strategy is
superior to a variety of popular hand-crafted alternatives. Experiments also
show that (i) features extracted from CNNs are robust to compression, (ii) CNNs
trained on non-document images transfer well to document analysis tasks, and
(iii) enforcing region-specific feature-learning is unnecessary given
sufficient training data. This work also makes available a new labelled subset
of the IIT-CDIP collection, containing 400,000 document images across 16
categories, useful for training new CNNs for document analysis.

negative:
The standard loss functions used in the literature on probabilistic
prediction are the log loss function, the Brier loss function, and the
spherical loss function; however, any computable proper loss function can be
used for comparison of prediction algorithms. This note shows that the log loss
function is most selective in that any prediction algorithm that is optimal for
a given data sequence (in the sense of the algorithmic theory of randomness)
under the log loss function will be optimal under any computable proper mixable
loss function; on the other hand, there is a data sequence and a prediction
algorithm that is optimal for that sequence under either of the two other
standard loss functions but not under the log loss function.

negative:
In experimental design, we are given a large collection of vectors, each with
a hidden response value that we assume derives from an underlying linear model,
and we wish to pick a small subset of the vectors such that querying the
corresponding responses will lead to a good estimator of the model. A classical
approach in statistics is to assume the responses are linear, plus zero-mean
i.i.d. Gaussian noise, in which case the goal is to provide an unbiased
estimator with smallest mean squared error (A-optimal design). A related
approach, more common in computer science, is to assume the responses are
arbitrary but fixed, in which case the goal is to estimate the least squares
solution using few responses, as quickly as possible, for worst-case inputs.
Despite many attempts, characterizing the relationship between these two
approaches has proven elusive. We address this by proposing a framework for
experimental design where the responses are produced by an arbitrary unknown
distribution. We show that there is an efficient randomized experimental design
procedure that achieves strong variance bounds for an unbiased estimator using
few responses in this general model. Nearly tight bounds for the classical
A-optimality criterion, as well as improved bounds for worst-case responses,
emerge as special cases of this result. In the process, we develop a new
algorithm for a joint sampling distribution called volume sampling, and we
propose a new i.i.d. importance sampling method: inverse score sampling. A key
novelty of our analysis is in developing new expected error bounds for
worst-case regression by controlling the tail behavior of i.i.d. sampling via
the jointness of volume sampling. Our result motivates a new minimax-optimality
criterion for experimental design which can be viewed as an extension of both
A-optimal design and sampling for worst-case regression.

negative:
With the increase of information, document classification as one of the
methods of text mining, plays vital role in many management and organizing
information. Document classification is the process of assigning a document to
one or more predefined category labels. Document classification includes
different parts such as text processing, term selection, term weighting and
final classification. The accuracy of document classification is very
important. Thus improvement in each part of classification should lead to
better results and higher precision. Term weighting has a great impact on the
accuracy of the classification. Most of the existing weighting methods exploit
the statistical information of terms in documents and do not consider semantic
relations between words. In this paper, an automated document classification
system is presented that uses a novel term weighting method based on semantic
relations between terms. To evaluate the proposed method, three standard
Persian corpuses are used. Experiment results show 2 to 4 percent improvement
in classification accuracy compared with the best previous designed system for
Persian documents.

negative:
Document clustering is a text mining technique used to provide better
document search and browsing in digital libraries or online corpora. A lot of
research has been done on biomedical document clustering that is based on using
existing ontology. But, associations and co-occurrences of the medical concepts
are not well represented by using ontology. In this research, a vector
representation of concepts of diseases and similarity measurement between
concepts are proposed. They identify the closest concepts of diseases in the
context of a corpus. Each document is represented by using the vector space
model. A weight scheme is proposed to consider both local content and
associations between concepts. A Self-Organizing Map is used as document
clustering algorithm. The vector projection and visualization features of SOM
enable visualization and analysis of the clusters distributions and
relationships on the two dimensional space. The experimental results show that
the proposed document clustering framework generates meaningful clusters and
facilitate visualization of the clusters based on the concepts of diseases.

negative:
We consider the problem of engineering robust direct perception neural
networks with output being regression. Such networks take high dimensional
input image data, and they produce affordances such as the curvature of the
upcoming road segment or the distance to the front vehicle. Our proposal starts
by allowing a neural network prediction to deviate from the label with
tolerance $\Delta$. The source of tolerance can be either contractual or from
limiting factors where two entities may label the same data with slightly
different numerical values. The tolerance motivates the use of a non-standard
loss function where the loss is set to $0$ so long as the prediction-to-label
distance is less than $\Delta$. We further extend the loss function and define
a new provably robust criterion that is parametric to the allowed output
tolerance $\Delta$, the layer index $\tilde{l}$ where perturbation is
considered, and the maximum perturbation amount $\kappa$. During training, the
robust loss is computed by first propagating symbolic errors from the
$\tilde{l}$-th layer (with quantity bounded by $\kappa$) to the output layer,
followed by computing the overflow between the error bounds and the allowed
tolerance. The overall concept is experimented in engineering a direct
perception neural network for understanding the central position of the
ego-lane in pixel coordinates.

negative:
One major drawback of state of the art Neural Networks (NN)-based approaches
for document classification purposes is the large number of training samples
required to obtain an efficient classification. The minimum required number is
around one thousand annotated documents for each class. In many cases it is
very difficult, if not impossible, to gather this number of samples in real
industrial processes. In this paper, we analyse the efficiency of NN-based
document classification systems in a sub-optimal training case, based on the
situation of a company document stream. We evaluated three different
approaches, one based on image content and two on textual content. The
evaluation was divided into four parts: a reference case, to assess the
performance of the system in the lab; two cases that each simulate a specific
difficulty linked to document stream processing; and a realistic case that
combined all of these difficulties. The realistic case highlighted the fact
that there is a significant drop in the efficiency of NN-Based document
classification systems. Although they remain efficient for well represented
classes (with an over-fitting of the system for those classes), it is
impossible for them to handle appropriately less well represented classes.
NN-Based document classification systems need to be adapted to resolve these
two problems before they can be considered for use in a company document
stream.

negative:
Given a finite set of unknown distributions, or arms, that can be sampled, we
consider the problem of identifying the one with the largest mean using a
delta-correct algorithm (an adaptive, sequential algorithm that restricts the
probability of error to a specified delta) that has minimum sample complexity.
Lower bounds for delta-correct algorithms are well known. Delta-correct
algorithms that match the lower bound asymptotically as delta reduces to zero
have been previously developed when arm distributions are restricted to a
single parameter exponential family. In this paper, we first observe a negative
result that some restrictions are essential, as otherwise under a delta-correct
algorithm, distributions with unbounded support would require an infinite
number of samples in expectation. We then propose a delta-correct algorithm
that matches the lower bound as delta reduces to zero under the mild
restriction that a known bound on the expectation of a non-negative,
continuous, increasing convex function (for example, the squared moment) of the
underlying random variables, exists. We also propose batch processing and
identify near-optimal batch sizes to substantially speed up the proposed
algorithm. The best-arm problem has many learning applications, including
recommendation systems and product selection. It is also a well studied classic
problem in the simulation community.

negative:
Recent approaches in literature have exploited the multi-modal information in
documents (text, layout, image) to serve specific downstream document tasks.
However, they are limited by their - (i) inability to learn cross-modal
representations across text, layout and image dimensions for documents and (ii)
inability to process multi-page documents. Pre-training techniques have been
shown in Natural Language Processing (NLP) domain to learn generic textual
representations from large unlabelled datasets, applicable to various
downstream NLP tasks. In this paper, we propose a multi-task learning-based
framework that utilizes a combination of self-supervised and supervised
pre-training tasks to learn a generic document representation applicable to
various downstream document tasks. Specifically, we introduce Document Topic
Modelling and Document Shuffle Prediction as novel pre-training tasks to learn
rich image representations along with the text and layout representations for
documents. We utilize the Longformer network architecture as the backbone to
encode the multi-modal information from multi-page documents in an end-to-end
fashion. We showcase the applicability of our pre-training framework on a
variety of different real-world document tasks such as document classification,
document information extraction, and document retrieval. We evaluate our
framework on different standard document datasets and conduct exhaustive
experiments to compare performance against various ablations of our framework
and state-of-the-art baselines.

negative:
We introduce a method to stabilize Generative Adversarial Networks (GANs) by
defining the generator objective with respect to an unrolled optimization of
the discriminator. This allows training to be adjusted between using the
optimal discriminator in the generator's objective, which is ideal but
infeasible in practice, and using the current value of the discriminator, which
is often unstable and leads to poor solutions. We show how this technique
solves the common problem of mode collapse, stabilizes training of GANs with
complex recurrent generators, and increases diversity and coverage of the data
distribution by the generator.

negative:
In few-shot learning, typically, the loss function which is applied at test
time is the one we are ultimately interested in minimising, such as the
mean-squared-error loss for a regression problem. However, given that we have
few samples at test time, we argue that the loss function that we are
interested in minimising is not necessarily the loss function most suitable for
computing gradients in a few-shot setting. We propose VIABLE, a generic
meta-learning extension that builds on existing meta-gradient-based methods by
learning a differentiable loss function, replacing the pre-defined inner-loop
loss function in performing task-specific updates. We show that learning a loss
function capable of leveraging relational information between samples reduces
underfitting, and significantly improves performance and sample efficiency on a
simple regression task. Furthermore, we show VIABLE is scalable by evaluating
on the Mini-Imagenet dataset.

negative:
In this paper, we explore text classification with extremely weak
supervision, i.e., only relying on the surface text of class names. This is a
more challenging setting than the seed-driven weak supervision, which allows a
few seed words per class. We opt to attack this problem from a representation
learning perspective -- ideal document representations should lead to nearly
the same results between clustering and the desired classification. In
particular, one can classify the same corpus differently (e.g., based on topics
and locations), so document representations should be adaptive to the given
class names. We propose a novel framework X-Class to realize the adaptive
representations. Specifically, we first estimate class representations by
incrementally adding the most similar word to each class until inconsistency
arises. Following a tailored mixture of class attention mechanisms, we obtain
the document representation via a weighted average of contextualized word
representations. With the prior of each document assigned to its nearest class,
we then cluster and align the documents to classes. Finally, we pick the most
confident documents from each cluster to train a text classifier. Extensive
experiments demonstrate that X-Class can rival and even outperform seed-driven
weakly supervised methods on 7 benchmark datasets. Our dataset and code are
released at https://github.com/ZihanWangKi/XClass/ .

negative:
Physics-informed neural networks (PINNs) are effective in solving inverse
problems based on differential and integral equations with sparse, noisy,
unstructured, and multi-fidelity data. PINNs incorporate all available
information into a loss function, thus recasting the original problem into an
optimization problem. In this paper, we extend PINNs to parameter and function
inference for integral equations such as nonlocal Poisson and nonlocal
turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The
contribution of the paper is three-fold. First, we propose a unified nonlocal
operator, which converges to the classical Laplacian as one of the operator
parameters, the nonlocal interaction radius $\delta$ goes to zero, and to the
fractional Laplacian as $\delta$ goes to infinity. This universal operator
forms a super-set of classical Laplacian and fractional Laplacian operators
and, thus, has the potential to fit a broad spectrum of data sets. We provide
theoretical convergence rates with respect to $\delta$ and verify them via
numerical experiments. Second, we use nPINNs to estimate the two parameters,
$\delta$ and $\alpha$. The strong non-convexity of the loss function yielding
multiple (good) local minima reveals the occurrence of the operator mimicking
phenomenon: different pairs of estimated parameters could produce multiple
solutions of comparable accuracy. Third, we propose another nonlocal operator
with spatially variable order $\alpha(y)$, which is more suitable for modeling
turbulent Couette flow. Our results show that nPINNs can jointly infer this
function as well as $\delta$. Also, these parameters exhibit a universal
behavior with respect to the Reynolds number, a finding that contributes to our
understanding of nonlocal interactions in wall-bounded turbulence.

negative:
Machine learning has the potential to assist many communities in using the
large datasets that are becoming more and more available. Unfortunately, much
of that potential is not being realized because it would require sharing data
in a way that compromises privacy. In order to overcome this hurdle, several
methods have been proposed that generate synthetic data while preserving the
privacy of the real data. In this paper we consider a key characteristic that
synthetic data should have in order to be useful for machine learning
researchers - the relative performance of two algorithms (trained and tested)
on the synthetic dataset should be the same as their relative performance (when
trained and tested) on the original dataset.

negative:
We propose a Label Propagation based algorithm for weakly supervised text
classification. We construct a graph where each document is represented by a
node and edge weights represent similarities among the documents. Additionally,
we discover underlying topics using Latent Dirichlet Allocation (LDA) and
enrich the document graph by including the topics in the form of additional
nodes. The edge weights between a topic and a text document represent level of
"affinity" between them. Our approach does not require document level
labelling, instead it expects manual labels only for topic nodes. This
significantly minimizes the level of supervision needed as only a few topics
are observed to be enough for achieving sufficiently high accuracy. The Label
Propagation Algorithm is employed on this enriched graph to propagate labels
among the nodes. Our approach combines the advantages of Label Propagation
(through document-document similarities) and Topic Modelling (for minimal but
smart supervision). We demonstrate the effectiveness of our approach on various
datasets and compare with state-of-the-art weakly supervised text
classification approaches.

negative:
We introduce a novel type of text representation that preserves the 2D layout
of a document. This is achieved by encoding each document page as a
two-dimensional grid of characters. Based on this representation, we present a
generic document understanding pipeline for structured documents. This pipeline
makes use of a fully convolutional encoder-decoder network that predicts a
segmentation mask and bounding boxes. We demonstrate its capabilities on an
information extraction task from invoices and show that it significantly
outperforms approaches based on sequential text or document images.

negative:
Rapid increase of digitized document give birth to high demand of document
image retrieval. While conventional document image retrieval approaches depend
on complex OCR-based text recognition and text similarity detection, this paper
proposes a new content-based approach, in which more attention is paid to
features extraction and fusion. In the proposed approach, multiple features of
document images are extracted by different CNN models. After that, the
extracted CNN features are reduced and fused into weighted average feature.
Finally, the document images are ranked based on feature similarity to a
provided query image. Experimental procedure is performed on a group of
document images that transformed from academic papers, which contain both
English and Chinese document, the results show that the proposed approach has
good ability to retrieve document images with similar text content, and the
fusion of CNN features can effectively improve the retrieval accuracy.

negative:
In recent years online shopping has gained momentum and became an important
venue for customers wishing to save time and simplify their shopping process. A
key advantage of shopping online is the ability to read what other customers
are saying about products of interest. In this work, we aim to maintain this
advantage in situations where extreme brevity is needed, for example, when
shopping by voice. We suggest a novel task of extracting a single
representative helpful sentence from a set of reviews for a given product. The
selected sentence should meet two conditions: first, it should be helpful for a
purchase decision and second, the opinion it expresses should be supported by
multiple reviewers. This task is closely related to the task of Multi Document
Summarization in the product reviews domain but differs in its objective and
its level of conciseness. We collect a dataset in English of sentence
helpfulness scores via crowd-sourcing and demonstrate its reliability despite
the inherent subjectivity involved. Next, we describe a complete model that
extracts representative helpful sentences with positive and negative sentiment
towards the product and demonstrate that it outperforms several baselines.

negative:
In this article I describe a research agenda for securing machine learning
models against adversarial inputs at test time. This article does not present
results but instead shares some of my thoughts about where I think that the
field needs to go. Modern machine learning works very well on I.I.D. data: data
for which each example is drawn {\em independently} and for which the
distribution generating each example is {\em identical}. When these assumptions
are relaxed, modern machine learning can perform very poorly. When machine
learning is used in contexts where security is a concern, it is desirable to
design models that perform well even when the input is designed by a malicious
adversary. So far most research in this direction has focused on an adversary
who violates the {\em identical} assumption, and imposes some kind of
restricted worst-case distribution shift. I argue that machine learning
security researchers should also address the problem of relaxing the {\em
independence} assumption and that current strategies designed for robustness to
distribution shift will not do so. I recommend {\em dynamic models} that change
each time they are run as a potential solution path to this problem, and show
an example of a simple attack using correlated data that can be mitigated by a
simple dynamic defense. This is not intended as a real-world security measure,
but as a recommendation to explore this research direction and develop more
realistic defenses.

negative:
Most of the textual information available to us are temporally variable. In a
world where information is dynamic, time-stamping them is a very important
task. Documents are a good source of information and are used for many tasks
like, sentiment analysis, classification of reviews etc. The knowledge of
creation date of documents facilitates several tasks like summarization, event
extraction, temporally focused information extraction etc. Unfortunately, for
most of the documents on the web, the time-stamp meta-data is either erroneous
or missing. Thus document dating is a challenging problem which requires
inference over the temporal structure of the document alongside the contextual
information of the document. Prior document dating systems have largely relied
on handcrafted features while ignoring such document-internal structures. In
this paper we propose NeuralDater, a Graph Convolutional Network (GCN) based
document dating approach which jointly exploits syntactic and temporal graph
structures of document in a principled way. We also pointed out some
limitations of NeuralDater and tried to utilize both context and temporal
information in documents in a more flexible and intuitive manner proposing AD3:
Attentive Deep Document Dater, an attention-based document dating system. To
the best of our knowledge these are the first application of deep learning
methods for the task. Through extensive experiments on real-world datasets, we
find that our models significantly outperforms state-of-the-art baselines by a
significant margin.

negative:
In this paper, we propose a novel {\em $e$-exponentiated} transformation, $0
\le e<1$, for loss functions. When the transformation is applied to a convex
loss function, the transformed loss function become more robust to outliers.
Using a novel generalization error bound, we have theoretically shown that the
transformed loss function has a tighter bound for datasets corrupted by
outliers. Our empirical observation shows that the accuracy obtained using the
transformed loss function can be significantly better than the same obtained
using the original loss function and comparable to that obtained by some other
state of the art methods in the presence of label noise.

negative:
Extracting information from full documents is an important problem in many
domains, but most previous work focus on identifying relationships within a
sentence or a paragraph. It is challenging to create a large-scale information
extraction (IE) dataset at the document level since it requires an
understanding of the whole document to annotate entities and their
document-level relationships that usually span beyond sentences or even
sections. In this paper, we introduce SciREX, a document level IE dataset that
encompasses multiple IE tasks, including salient entity identification and
document level $N$-ary relation identification from scientific articles. We
annotate our dataset by integrating automatic and human annotations, leveraging
existing scientific knowledge resources. We develop a neural model as a strong
baseline that extends previous state-of-the-art IE models to document-level IE.
Analyzing the model performance shows a significant gap between human
performance and current baselines, inviting the community to use our dataset as
a challenge to develop document-level IE models. Our data and code are publicly
available at https://github.com/allenai/SciREX

negative:
We consider the problem of learning an unknown function $f_{\star}$ on the
$d$-dimensional sphere with respect to the square loss, given i.i.d. samples
$\{(y_i,{\boldsymbol x}_i)\}_{i\le n}$ where ${\boldsymbol x}_i$ is a feature
vector uniformly distributed on the sphere and $y_i=f_{\star}({\boldsymbol
x}_i)+\varepsilon_i$. We study two popular classes of models that can be
regarded as linearizations of two-layers neural networks around a random
initialization: the random features model of Rahimi-Recht (RF); the neural
tangent kernel model of Jacot-Gabriel-Hongler (NT). Both these approaches can
also be regarded as randomized approximations of kernel ridge regression (with
respect to different kernels), and enjoy universal approximation properties
when the number of neurons $N$ diverges, for a fixed dimension $d$.
  We consider two specific regimes: the approximation-limited regime, in which
$n=\infty$ while $d$ and $N$ are large but finite; and the sample size-limited
regime in which $N=\infty$ while $d$ and $n$ are large but finite. In the first
regime we prove that if $d^{\ell + \delta} \le N\le d^{\ell+1-\delta}$ for
small $\delta > 0$, then \RF\, effectively fits a degree-$\ell$ polynomial in
the raw features, and \NT\, fits a degree-$(\ell+1)$ polynomial. In the second
regime, both RF and NT reduce to kernel methods with rotationally invariant
kernels. We prove that, if the number of samples is $d^{\ell + \delta} \le n
\le d^{\ell +1-\delta}$, then kernel methods can fit at most a a degree-$\ell$
polynomial in the raw features. This lower bound is achieved by kernel ridge
regression. Optimal prediction error is achieved for vanishing ridge
regularization.

negative:
In the negative perceptron problem we are given $n$ data points
$({\boldsymbol x}_i,y_i)$, where ${\boldsymbol x}_i$ is a $d$-dimensional
vector and $y_i\in\{+1,-1\}$ is a binary label. The data are not linearly
separable and hence we content ourselves to find a linear classifier with the
largest possible \emph{negative} margin. In other words, we want to find a unit
norm vector ${\boldsymbol \theta}$ that maximizes $\min_{i\le n}y_i\langle
{\boldsymbol \theta},{\boldsymbol x}_i\rangle$. This is a non-convex
optimization problem (it is equivalent to finding a maximum norm vector in a
polytope), and we study its typical properties under two random models for the
data.
  We consider the proportional asymptotics in which $n,d\to \infty$ with
$n/d\to\delta$, and prove upper and lower bounds on the maximum margin
$\kappa_{\text{s}}(\delta)$ or -- equivalently -- on its inverse function
$\delta_{\text{s}}(\kappa)$. In other words, $\delta_{\text{s}}(\kappa)$ is the
overparametrization threshold: for $n/d\le
\delta_{\text{s}}(\kappa)-\varepsilon$ a classifier achieving vanishing
training error exists with high probability, while for $n/d\ge
\delta_{\text{s}}(\kappa)+\varepsilon$ it does not. Our bounds on
$\delta_{\text{s}}(\kappa)$ match to the leading order as $\kappa\to -\infty$.
We then analyze a linear programming algorithm to find a solution, and
characterize the corresponding threshold $\delta_{\text{lin}}(\kappa)$. We
observe a gap between the interpolation threshold $\delta_{\text{s}}(\kappa)$
and the linear programming threshold $\delta_{\text{lin}}(\kappa)$, raising the
question of the behavior of other algorithms.

negative:
We study distributionally robust optimization (DRO) with Sinkhorn distance --
a variant of Wasserstein distance based on entropic regularization. We provide
convex programming dual reformulation for a general nominal distribution.
Compared with Wasserstein DRO, it is computationally tractable for a larger
class of loss functions, and its worst-case distribution is more reasonable. We
propose an efficient first-order algorithm with bisection search to solve the
dual reformulation. We demonstrate that our proposed algorithm finds
$\delta$-optimal solution of the new DRO formulation with computation cost
$\tilde{O}(\delta^{-3})$ and memory cost $\tilde{O}(\delta^{-2})$, and the
computation cost further improves to $\tilde{O}(\delta^{-2})$ when the loss
function is smooth. Finally, we provide various numerical examples using both
synthetic and real data to demonstrate its competitive performance and light
computational speed.

negative:
Motivated by applications in crowdsourced entity resolution in database,
signed edge prediction in social networks and correlation clustering, Mazumdar
and Saha [NIPS 2017] proposed an elegant theoretical model for studying
clustering with a faulty oracle. In this model, given a set of $n$ items which
belong to $k$ unknown groups (or clusters), our goal is to recover the clusters
by asking pairwise queries to an oracle. This oracle can answer the query that
``do items $u$ and $v$ belong to the same cluster?''. However, the answer to
each pairwise query errs with probability $\varepsilon$, for some
$\varepsilon\in(0,\frac12)$. Mazumdar and Saha provided two algorithms under
this model: one algorithm is query-optimal while time-inefficient (i.e.,
running in quasi-polynomial time), the other is time efficient (i.e., in
polynomial time) while query-suboptimal. Larsen, Mitzenmacher and Tsourakakis
[WWW 2020] then gave a new time-efficient algorithm for the special case of $2$
clusters, which is query-optimal if the bias $\delta:=1-2\varepsilon$ of the
model is large. It was left as an open question whether one can obtain a
query-optimal, time-efficient algorithm for the general case of $k$ clusters
and other regimes of $\delta$.
  In this paper, we make progress on the above question and provide a
time-efficient algorithm with nearly-optimal query complexity (up to a factor
of $O(\log^2 n)$) for all constant $k$ and any $\delta$ in the regime when
information-theoretic recovery is possible. Our algorithm is built on a
connection to the stochastic block model.

negative:
We introduce a novel approach for scanned document representation to perform
field extraction. It allows the simultaneous encoding of the textual, visual
and layout information in a 3-axis tensor used as an input to a segmentation
model. We improve the recent Chargrid and Wordgrid \cite{chargrid} models in
several ways, first by taking into account the visual modality, then by
boosting its robustness in regards to small datasets while keeping the
inference time low. Our approach is tested on public and private document-image
datasets, showing higher performances compared to the recent state-of-the-art
methods.

negative:
In unconstrained optimisation on an Euclidean space, to prove convergence in
Gradient Descent processes (GD) $x_{n+1}=x_n-\delta _n \nabla f(x_n)$ it
usually is required that the learning rates $\delta _n$'s are bounded: $\delta
_n\leq \delta $ for some positive $\delta $. Under this assumption, if the
sequence $x_n$ converges to a critical point $z$, then with large values of $n$
the update will be small because $||x_{n+1}-x_n||\lesssim ||\nabla f(x_n)||$.
This may also force the sequence to converge to a bad minimum. If we can allow,
at least theoretically, that the learning rates $\delta _n$'s are not bounded,
then we may have better convergence to better minima.
  A previous joint paper by the author showed convergence for the usual version
of Backtracking GD under very general assumptions on the cost function $f$. In
this paper, we allow the learning rates $\delta _n$ to be unbounded, in the
sense that there is a function $h:(0,\infty)\rightarrow (0,\infty )$ such that
$\lim _{t\rightarrow 0}th(t)=0$ and $\delta _n\lesssim \max \{h(x_n),\delta \}$
satisfies Armijo's condition for all $n$, and prove convergence under the same
assumptions as in the mentioned paper. It will be shown that this growth rate
of $h$ is best possible if one wants convergence of the sequence $\{x_n\}$.
  A specific way for choosing $\delta _n$ in a discrete way connects to Two-way
Backtracking GD defined in the mentioned paper. We provide some results which
either improve or are implicitly contained in those in the mentioned paper and
another recent paper on avoidance of saddle points.

negative:
In this work, a region-based Deep Convolutional Neural Network framework is
proposed for document structure learning. The contribution of this work
involves efficient training of region based classifiers and effective
ensembling for document image classification. A primary level of `inter-domain'
transfer learning is used by exporting weights from a pre-trained VGG16
architecture on the ImageNet dataset to train a document classifier on whole
document images. Exploiting the nature of region based influence modelling, a
secondary level of `intra-domain' transfer learning is used for rapid training
of deep learning models for image segments. Finally, stacked generalization
based ensembling is utilized for combining the predictions of the base deep
neural network models. The proposed method achieves state-of-the-art accuracy
of 92.2% on the popular RVL-CDIP document image dataset, exceeding benchmarks
set by existing algorithms.

negative:
This paper summarizes our entries to both subtasks of the first DialDoc
shared task which focuses on the agent response prediction task in
goal-oriented document-grounded dialogs. The task is split into two subtasks:
predicting a span in a document that grounds an agent turn and generating an
agent response based on a dialog and grounding document. In the first subtask,
we restrict the set of valid spans to the ones defined in the dataset, use a
biaffine classifier to model spans, and finally use an ensemble of different
models. For the second subtask, we use a cascaded model which grounds the
response prediction on the predicted span instead of the full document. With
these approaches, we obtain significant improvements in both subtasks compared
to the baseline.

negative:
We study the problem of testing identity against a given distribution with a
focus on the high confidence regime. More precisely, given samples from an
unknown distribution $p$ over $n$ elements, an explicitly given distribution
$q$, and parameters $0< \epsilon, \delta < 1$, we wish to distinguish, {\em
with probability at least $1-\delta$}, whether the distributions are identical
versus $\varepsilon$-far in total variation distance. Most prior work focused
on the case that $\delta = \Omega(1)$, for which the sample complexity of
identity testing is known to be $\Theta(\sqrt{n}/\epsilon^2)$. Given such an
algorithm, one can achieve arbitrarily small values of $\delta$ via black-box
amplification, which multiplies the required number of samples by
$\Theta(\log(1/\delta))$.
  We show that black-box amplification is suboptimal for any $\delta = o(1)$,
and give a new identity tester that achieves the optimal sample complexity. Our
new upper and lower bounds show that the optimal sample complexity of identity
testing is \[
  \Theta\left( \frac{1}{\epsilon^2}\left(\sqrt{n \log(1/\delta)} +
\log(1/\delta) \right)\right) \] for any $n, \varepsilon$, and $\delta$. For
the special case of uniformity testing, where the given distribution is the
uniform distribution $U_n$ over the domain, our new tester is surprisingly
simple: to test whether $p = U_n$ versus $d_{\mathrm TV}(p, U_n) \geq
\varepsilon$, we simply threshold $d_{\mathrm TV}(\widehat{p}, U_n)$, where
$\widehat{p}$ is the empirical probability distribution. The fact that this
simple "plug-in" estimator is sample-optimal is surprising, even in the
constant $\delta$ case. Indeed, it was believed that such a tester would not
attain sublinear sample complexity even for constant values of $\varepsilon$
and $\delta$.

negative:
Over the past decades, researchers have been pushing the limits of Deep
Reinforcement Learning (DRL). Although DRL has attracted substantial interest
from practitioners, many are blocked by having to search through a plethora of
available methodologies that are seemingly alike, while others are still
building RL agents from scratch based on classical theories. To address the
aforementioned gaps in adopting the latest DRL methods, I am particularly
interested in testing out if any of the recent technology developed by the
leads in the field can be readily applied to a class of optimal trading
problems. Unsurprisingly, many prominent breakthroughs in DRL are investigated
and tested on strategic games: from AlphaGo to AlphaStar and at about the same
time, OpenAI Five. Thus, in this writing, I want to show precisely how to use a
DRL library that is initially built for games in a fundamental trading problem;
mean reversion. And by introducing a framework that incorporates
economically-motivated function properties, I also demonstrate, through the
library, a highly-performant and convergent DRL solution to decision-making
financial problems in general.

negative:
Document date is essential for many important tasks, such as document
retrieval, summarization, event detection, etc. While existing approaches for
these tasks assume accurate knowledge of the document date, this is not always
available, especially for arbitrary documents from the Web. Document Dating is
a challenging problem which requires inference over the temporal structure of
the document. Prior document dating systems have largely relied on handcrafted
features while ignoring such document internal structures. In this paper, we
propose NeuralDater, a Graph Convolutional Network (GCN) based document dating
approach which jointly exploits syntactic and temporal graph structures of
document in a principled way. To the best of our knowledge, this is the first
application of deep learning for the problem of document dating. Through
extensive experiments on real-world datasets, we find that NeuralDater
significantly outperforms state-of-the-art baseline by 19% absolute (45%
relative) accuracy points.

negative:
We show that Bayes' rule provides an effective mechanism for creating
document translation models that can be learned from only parallel sentences
and monolingual documents---a compelling benefit as parallel documents are not
always available. In our formulation, the posterior probability of a candidate
translation is the product of the unconditional (prior) probability of the
candidate output document and the "reverse translation probability" of
translating the candidate output back into the source language. Our proposed
model uses a powerful autoregressive language model as the prior on target
language documents, but it assumes that each sentence is translated
independently from the target to the source language. Crucially, at test time,
when a source document is observed, the document language model prior induces
dependencies between the translations of the source sentences in the posterior.
The model's independence assumption not only enables efficient use of available
data, but it additionally admits a practical left-to-right beam-search
algorithm for carrying out inference. Experiments show that our model benefits
from using cross-sentence context in the language model, and it outperforms
existing document translation approaches.

negative:
Neural networks with deep architectures have demonstrated significant
performance improvements in computer vision, speech recognition, and natural
language processing. The challenges in information retrieval (IR), however, are
different from these other application areas. A common form of IR involves
ranking of documents--or short passages--in response to keyword-based queries.
Effective IR systems must deal with query-document vocabulary mismatch problem,
by modeling relationships between different query and document terms and how
they indicate relevance. Models should also consider lexical matches when the
query contains rare terms--such as a person's name or a product model
number--not seen during training, and to avoid retrieving semantically related
but irrelevant results. In many real-life IR tasks, the retrieval involves
extremely large collections--such as the document index of a commercial Web
search engine--containing billions of documents. Efficient IR methods should
take advantage of specialized IR data structures, such as inverted index, to
efficiently retrieve from large collections. Given an information need, the IR
system also mediates how much exposure an information artifact receives by
deciding whether it should be displayed, and where it should be positioned,
among other results. Exposure-aware IR systems may optimize for additional
objectives, besides relevance, such as parity of exposure for retrieved items
and content publishers. In this thesis, we present novel neural architectures
and methods motivated by the specific needs and challenges of IR tasks.

negative:
Document categorization is a technique where the category of a document is
determined. In this paper three well-known supervised learning techniques which
are Support Vector Machine(SVM), Na\"ive Bayes(NB) and Stochastic Gradient
Descent(SGD) compared for Bengali document categorization. Besides classifier,
classification also depends on how feature is selected from dataset. For
analyzing those classifier performances on predicting a document against twelve
categories several feature selection techniques are also applied in this
article namely Chi square distribution, normalized TFIDF (term
frequency-inverse document frequency) with word analyzer. So, we attempt to
explore the efficiency of those three-classification algorithms by using two
different feature selection techniques in this article.

negative:
We derive the optimal $(0, \delta)$-differentially private query-output
independent noise-adding mechanism for single real-valued query function under
a general cost-minimization framework. Under a mild technical condition, we
show that the optimal noise probability distribution is a uniform distribution
with a probability mass at the origin. We explicitly derive the optimal noise
distribution for general $\ell^p$ cost functions, including $\ell^1$ (for noise
magnitude) and $\ell^2$ (for noise power) cost functions, and show that the
probability concentration on the origin occurs when $\delta > \frac{p}{p+1}$.
Our result demonstrates an improvement over the existing Gaussian mechanisms by
a factor of two and three for $(0,\delta)$-differential privacy in the high
privacy regime in the context of minimizing the noise magnitude and noise
power, and the gain is more pronounced in the low privacy regime. Our result is
consistent with the existing result for $(0,\delta)$-differential privacy in
the discrete setting, and identifies a probability concentration phenomenon in
the continuous setting.

negative:
We consider the problem of maximizing non-negative non-decreasing set
functions. Although most of the recent work focus on exploiting submodularity,
it turns out that several objectives we encounter in practice are not
submodular. Nonetheless, often we leverage the greedy algorithms used in
submodular functions to determine a solution to the non-submodular functions.
Hereafter, we propose to address the original problem by \emph{approximating}
the non-submodular function and analyze the incurred error, as well as the
performance trade-offs. To quantify the approximation error, we introduce a
novel concept of $\delta$-approximation of a function, which we used to define
the space of submodular functions that lie within an approximation error. We
provide necessary conditions on the existence of such $\delta$-approximation
functions, which might not be unique. Consequently, we characterize this
subspace which we refer to as \emph{region of submodularity}. Furthermore,
submodular functions are known to lead to different sub-optimality guarantees,
so we generalize those dependencies upon a $\delta$-approximation into the
notion of \emph{greedy curvature}. Finally, we used this latter notion to
simplify some of the existing results and efficiently (i.e., linear complexity)
determine tightened bounds on the sub-optimality guarantees using objective
functions commonly used in practical setups and validate them using real data.

negative:
We study $(\epsilon, \delta)$-PAC best arm identification, where a
decision-maker must identify an $\epsilon$-optimal arm with probability at
least $1 - \delta$, while minimizing the number of arm pulls (samples). Most of
the work on this topic is in the sequential setting, where there is no
constraint on the time taken to identify such an arm; this allows the
decision-maker to pull one arm at a time. In this work, the decision-maker is
given a deadline of $T$ rounds, where, on each round, it can adaptively choose
which arms to pull and how many times to pull them; this distinguishes the
number of decisions made (i.e., time or number of rounds) from the number of
samples acquired (cost). Such situations occur in clinical trials, where one
may need to identify a promising treatment under a deadline while minimizing
the number of test subjects, or in simulation-based studies run on the cloud,
where we can elastically scale up or down the number of virtual machines to
conduct as many experiments as we wish, but need to pay for the resource-time
used. As the decision-maker can only make $T$ decisions, she may need to pull
some arms excessively relative to a sequential algorithm in order to perform
well on all possible problems. We formalize this added difficulty with two
hardness results that indicate that unlike sequential settings, the ability to
adapt to the problem difficulty is constrained by the finite deadline. We
propose Elastic Batch Racing (EBR), a novel algorithm for this setting and
bound its sample complexity, showing that EBR is optimal with respect to both
hardness results. We present simulations evaluating EBR in this setting, where
it outperforms baselines by several orders of magnitude.

negative:
We consider the stochastic bandit problem in the sublinear space setting,
where one cannot record the win-loss record for all $K$ arms. We give an
algorithm using $O(1)$ words of space with regret \[
  \sum_{i=1}^{K}\frac{1}{\Delta_i}\log \frac{\Delta_i}{\Delta}\log T \] where
$\Delta_i$ is the gap between the best arm and arm $i$ and $\Delta$ is the gap
between the best and the second-best arms. If the rewards are bounded away from
$0$ and $1$, this is within an $O(\log 1/\Delta)$ factor of the optimum regret
possible without space constraints.

negative:
This paper describes the short-term competition on the Components
Segmentation Task of Document Photos that was prepared in the context of the
16th International Conference on Document Analysis and Recognition (ICDAR
2021). This competition aims to bring together researchers working in the field
of identification document image processing and provides them a suitable
benchmark to compare their techniques on the component segmentation task of
document images. Three challenge tasks were proposed entailing different
segmentation assignments to be performed on a provided dataset. The collected
data are from several types of Brazilian ID documents, whose personal
information was conveniently replaced. There were 16 participants whose results
obtained for some or all the three tasks show different rates for the adopted
metrics, like Dice Similarity Coefficient ranging from 0.06 to 0.99. Different
Deep Learning models were applied by the entrants with diverse strategies to
achieve the best results in each of the tasks. Obtained results show that the
currently applied methods for solving one of the proposed tasks (document
boundary detection) are already well established. However, for the other two
challenge tasks (text zone and handwritten sign detection) research and
development of more robust approaches are still required to achieve acceptable
results.

negative:
We study the problem of identifying the best arm in a stochastic multi-armed
bandit game. Given a set of $n$ arms indexed from $1$ to $n$, each arm $i$ is
associated with an unknown reward distribution supported on $[0,1]$ with mean
$\theta_i$ and variance $\sigma_i^2$. Assume $\theta_1 > \theta_2 \geq \cdots
\geq\theta_n$. We propose an adaptive algorithm which explores the gaps and
variances of the rewards of the arms and makes future decisions based on the
gathered information using a novel approach called \textit{grouped median
elimination}. The proposed algorithm guarantees to output the best arm with
probability $(1-\delta)$ and uses at most $O \left(\sum_{i = 1}^n
\left(\frac{\sigma_i^2}{\Delta_i^2} + \frac{1}{\Delta_i}\right)(\ln \delta^{-1}
+ \ln \ln \Delta_i^{-1})\right)$ samples, where $\Delta_i$ ($i \geq 2$) denotes
the reward gap between arm $i$ and the best arm and we define $\Delta_1 =
\Delta_2$. This achieves a significant advantage over the variance-independent
algorithms in some favorable scenarios and is the first result that removes the
extra $\ln n$ factor on the best arm compared with the state-of-the-art. We
further show that $\Omega \left( \sum_{i = 1}^n \left(
\frac{\sigma_i^2}{\Delta_i^2} + \frac{1}{\Delta_i} \right) \ln \delta^{-1}
\right)$ samples are necessary for an algorithm to achieve the same goal,
thereby illustrating that our algorithm is optimal up to doubly logarithmic
terms.

negative:
In this paper, we consider non-convex optimization problems under
\textit{unknown} yet safety-critical constraints. Such problems naturally arise
in a variety of domains including robotics, manufacturing, and medical
procedures, where it is infeasible to know or identify all the constraints.
Therefore, the parameter space should be explored in a conservative way to
ensure that none of the constraints are violated during the optimization
process once we start from a safe initialization point. To this end, we develop
an algorithm called Reliable Frank-Wolfe (Reliable-FW). Given a general
non-convex function and an unknown polytope constraint, Reliable-FW
simultaneously learns the landscape of the objective function and the boundary
of the safety polytope. More precisely, by assuming that Reliable-FW has access
to a (stochastic) gradient oracle of the objective function and a noisy
feasibility oracle of the safety polytope, it finds an $\epsilon$-approximate
first-order stationary point with the optimal ${\mathcal{O}}({1}/{\epsilon^2})$
gradient oracle complexity (resp. $\tilde{\mathcal{O}}({1}/{\epsilon^3})$ (also
optimal) in the stochastic gradient setting), while ensuring the safety of all
the iterates. Rather surprisingly, Reliable-FW only makes
$\tilde{\mathcal{O}}(({d^2}/{\epsilon^2})\log 1/\delta)$ queries to the noisy
feasibility oracle (resp. $\tilde{\mathcal{O}}(({d^2}/{\epsilon^4})\log
1/\delta)$ in the stochastic gradient setting) where $d$ is the dimension and
$\delta$ is the reliability parameter, tightening the existing bounds even for
safe minimization of convex functions. We further specialize our results to the
case that the objective function is convex. A crucial component of our analysis
is to introduce and apply a technique called geometric shrinkage in the context
of safe optimization.

negative:
We consider the Max $K$-Armed Bandit problem, where a learning agent is faced
with several stochastic arms, each a source of i.i.d. rewards of unknown
distribution. At each time step the agent chooses an arm, and observes the
reward of the obtained sample. Each sample is considered here as a separate
item with the reward designating its value, and the goal is to find an item
with the highest possible value. Our basic assumption is a known lower bound on
the {\em tail function} of the reward distributions. Under the PAC framework,
we provide a lower bound on the sample complexity of any
$(\epsilon,\delta)$-correct algorithm, and propose an algorithm that attains
this bound up to logarithmic factors. We analyze the robustness of the proposed
algorithm and in addition, we compare the performance of this algorithm to the
variant in which the arms are not distinguishable by the agent and are chosen
randomly at each stage. Interestingly, when the maximal rewards of the arms
happen to be similar, the latter approach may provide better performance.