pwc-archive/papers-with-abstracts huggingface dataset:
Learning Adaptive Display Exposure for Real-Time Advertising
Authors: Weixun Wang, Junqi Jin, Jianye Hao, Chunjie Chen, Chuan Yu, Wei-Nan Zhang, Jun Wang, Xiaotian Hao, Yixi Wang, Han Li, Jian Xu, Kun Gai
Date: 2018-09-10 00:00:00
Tasks: Reinforcement Learning

In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal platform revenue and user experience. Consequently, major e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible ways to display ads. In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased? More specifically, we consider two types of constraints: request-level constraint ensures user experience for each user visit, and platform-level constraint controls the overall platform monetization rate. We model this problem as a Constrained Markov Decision Process with per-state constraint (psCMDP) and propose a constrained two-level reinforcement learning approach to decompose the original problem into two relatively independent sub-problems. To accelerate policy learning, we also devise a constrained hindsight experience replay mechanism. Experimental evaluations on industry-scale real-world datasets demonstrate the merits of our approach in both obtaining higher revenue under the constraints and the effectiveness of the constrained hindsight experience replay mechanism.
Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks
Authors: Seongsik Park, Seijoon Kim, Hyeokjun Choe, Sungroh Yoon
Date: 2018-09-10 00:00:00
Tasks: Image Classification

The spiking neural networks (SNNs) are considered as one of the most
promising artificial neural networks due to their energy efficient computing
capability. Recently, conversion of a trained deep neural network to an SNN has
improved the accuracy of deep SNNs. However, most of the previous studies have
not achieved satisfactory results in terms of inference speed and energy
efficiency. In this paper, we propose a fast and energy-efficient information
transmission method with burst spikes and hybrid neural coding scheme in deep
SNNs. Our experimental results showed the proposed methods can improve
inference energy efficiency and shorten the latency.
Deep MR Image Super-Resolution Using Structural Priors
Authors: Venkateswararao Cherukuri, Tiantong Guo, Steven J. Schiff, Vishal Monga
Date: 2018-09-10 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

High resolution magnetic resonance (MR) images are desired for accurate
diagnostics. In practice, image resolution is restricted by factors like
hardware, cost and processing constraints. Recently, deep learning methods have
been shown to produce compelling state of the art results for image
super-resolution. Paying particular attention to desired hi-resolution MR image
structure, we propose a new regularized network that exploits image priors,
namely a low-rank structure and a sharpness prior to enhance deep MR image
superresolution. Our contributions are then incorporating these priors in an
analytically tractable fashion in the learning of a convolutional neural
network (CNN) that accomplishes the super-resolution task. This is particularly
challenging for the low rank prior, since the rank is not a differentiable
function of the image matrix (and hence the network parameters), an issue we
address by pursuing differentiable approximations of the rank. Sharpness is
emphasized by the variance of the Laplacian which we show can be implemented by
a fixed {\em feedback} layer at the output of the network. Experiments
performed on two publicly available MR brain image databases exhibit promising
results particularly when training imagery is limited.
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers
Authors: Zhen He, Jian Li, Daxue Liu, Hangen He, David Barber
Date: 2018-09-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Multi-Object Tracking, Object Tracking, Online Multi-Object Tracking

Online Multi-Object Tracking (MOT) from videos is a challenging computer
vision task which has been extensively studied for decades. Most of the
existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm
combined with popular machine learning approaches which largely reduce the
human effort to tune algorithm parameters. However, the commonly used
supervised learning approaches require the labeled data (e.g., bounding boxes),
which is expensive for videos. Also, the TBD framework is usually suboptimal
since it is not end-to-end, i.e., it considers the task as detection and
tracking, but not jointly. To achieve both label-free and end-to-end learning
of MOT, we propose a Tracking-by-Animation framework, where a differentiable
neural model first tracks objects from input frames and then animates these
objects into reconstructed frames. Learning is then driven by the
reconstruction error through backpropagation. We further propose a
Reprioritized Attentive Tracking to improve the robustness of data association.
Experiments conducted on both synthetic and real video datasets show the
potential of the proposed model. Our project page is publicly available at:
https://github.com/zhen-he/tracking-by-animation
Greedy Search with Probabilistic N-gram Matching for Neural Machine Translation
Authors: Chenze Shao, Yang Feng, Xilin Chen
Date: 2018-09-10 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, NMT, Translation

Neural machine translation (NMT) models are usually trained with the
word-level loss using the teacher forcing algorithm, which not only evaluates
the translation improperly but also suffers from exposure bias. Sequence-level
training under the reinforcement framework can mitigate the problems of the
word-level loss, but its performance is unstable due to the high variance of
the gradient estimation. On these grounds, we present a method with a
differentiable sequence-level training objective based on probabilistic n-gram
matching which can avoid the reinforcement framework. In addition, this method
performs greedy search in the training which uses the predicted words as
context just as at inference to alleviate the problem of exposure bias.
Experiment results on the NIST Chinese-to-English translation tasks show that
our method significantly outperforms the reinforcement-based algorithms and
achieves an improvement of 1.5 BLEU points on average over a strong baseline
system.
LensKit for Python: Next-Generation Software for Recommender System Experiments
Authors: Michael D. Ekstrand
Date: 2018-09-10 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

LensKit is an open-source toolkit for building, researching, and learning about recommender systems. First released in 2010 as a Java framework, it has supported diverse published research, small-scale production deployments, and education in both MOOC and traditional classroom settings. In this paper, I present the next generation of the LensKit project, re-envisioning the original tool's objectives as flexible Python package for supporting recommender systems research and development. LensKit for Python (LKPY) enables researchers and students to build robust, flexible, and reproducible experiments that make use of the large and growing PyData and Scientific Python ecosystem, including scikit-learn, TensorFlow, and PyTorch. To that end, it provides classical collaborative filtering implementations, recommender system evaluation metrics, data preparation routines, and tools for efficiently batch running recommendation algorithms, all usable in any combination with each other or with other Python software. This paper describes the design goals, use cases, and capabilities of LKPY, contextualized in a reflection on the successes and failures of the original LensKit for Java software.
Memristive LSTM network hardware architecture for time-series predictive modeling problem
Authors: Kazybek Adam, Kamilya Smagulova, Alex Pappachen James
Date: 2018-09-10 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

Analysis of time-series data allows to identify long-term trends and make
predictions that can help to improve our lives. With the rapid development of
artificial neural networks, long short-term memory (LSTM) recurrent neural
network (RNN) configuration is found to be capable in dealing with time-series
forecasting problems where data points are time-dependent and possess
seasonality trends. Gated structure of LSTM cell and flexibility in network
topology (one-to-many, many-to-one, etc.) allows to model systems with multiple
input variables and control several parameters such as the size of the
look-back window to make a prediction and number of time steps to be predicted.
These make LSTM attractive tool over conventional methods such as
autoregression models, the simple average, moving average, naive approach,
ARIMA, Holt's linear trend method, Holt's Winter seasonal method, and others.
In this paper, we propose a hardware implementation of LSTM network
architecture for time-series forecasting problem. All simulations were
performed using TSMC 0.18um CMOS technology and HP memristor model.
A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification
Authors: Pengcheng Yang, Shuming Ma, Yi Zhang, Junyang Lin, Qi Su, Xu sun
Date: 2018-09-10 00:00:00
Tasks: Deep Reinforcement Learning, General Classification, Multi Label Text Classification, Multi-Label Text Classification, Reinforcement Learning, text-classification, Text Classification

Multi-label text classification (MLTC) aims to assign multiple labels to each
sample in the dataset. The labels usually have internal correlations. However,
traditional methods tend to ignore the correlations between labels. In order to
capture the correlations between labels, the sequence-to-sequence (Seq2Seq)
model views the MLTC task as a sequence generation problem, which achieves
excellent performance on this task. However, the Seq2Seq model is not suitable
for the MLTC task in essence. The reason is that it requires humans to
predefine the order of the output labels, while some of the output labels in
the MLTC task are essentially an unordered set rather than an ordered sequence.
This conflicts with the strict requirement of the Seq2Seq model for the label
order. In this paper, we propose a novel sequence-to-set framework utilizing
deep reinforcement learning, which not only captures the correlations between
labels, but also reduces the dependence on the label order. Extensive
experimental results show that our proposed method outperforms the competitive
baselines by a large margin.
Certified Adversarial Robustness with Additive Noise
Authors: Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin
Date: 2018-09-10 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Adversarial Attack, Adversarial Robustness

The existence of adversarial data examples has drawn significant attention in the deep-learning community; such data are seemingly minimally perturbed relative to the original data, but lead to very different outputs from a deep-learning algorithm. Although a significant body of work on developing defensive models has been considered, most such models are heuristic and are often vulnerable to adaptive attacks. Defensive methods that provide theoretical robustness guarantees have been studied intensively, yet most fail to obtain non-trivial robustness when a large-scale model and data are present. To address these limitations, we introduce a framework that is scalable and provides certified bounds on the norm of the input manipulation for constructing adversarial examples. We establish a connection between robustness against adversarial perturbation and additive random noise, and propose a training strategy that can significantly improve the certified bounds. Our evaluation on MNIST, CIFAR-10 and ImageNet suggests that the proposed method is scalable to complicated models and large data sets, while providing competitive robustness to state-of-the-art provable defense methods.
Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction
Authors: Lifeng Jin, Finale Doshi-Velez, Timothy Miller, William Schuler, Lane Schwartz
Date: 2018-09-10 00:00:00
Proceeding: EMNLP 2018 10

There have been several recent attempts to improve the accuracy of grammar
induction systems by bounding the recursive complexity of the induction model
(Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016; Jin et al.,
2018). Modern depth-bounded grammar inducers have been shown to be more
accurate than early unbounded PCFG inducers, but this technique has never been
compared against unbounded induction within the same system, in part because
most previous depth-bounding models are built around sequence models, the
complexity of which grows exponentially with the maximum allowed depth. The
present work instead applies depth bounds within a chart-based Bayesian PCFG
inducer (Johnson et al., 2007b), where bounding can be switched on and off, and
then samples trees with and without bounding. Results show that depth-bounding
is indeed significantly effective in limiting the search space of the inducer
and thereby increasing the accuracy of the resulting parsing model. Moreover,
parsing results on English, Chinese and German show that this bounded model
with a new inference technique is able to produce parse trees more accurately
than or competitively with state-of-the-art constituency-based grammar
induction models.
Regular omega-Languages with an Informative Right Congruence
Authors: Dana Angluin, Dana Fisman
Date: 2018-09-10 00:00:00

A regular language is almost fully characterized by its right congruence
relation. Indeed, a regular language can always be recognized by a DFA
isomorphic to the automaton corresponding to its right congruence, henceforth
the Rightcon automaton. The same does not hold for regular omega-languages. The
right congruence of a regular omega-language is not informative enough; many
regular omega-languages have a trivial right congruence, and in general it is
not always possible to define an omega-automaton recognizing a given language
that is isomorphic to the rightcon automaton.
  The class of weak regular omega-languages does have an informative right
congruence. That is, any weak regular omega-language can always be recognized
by a deterministic B\"uchi automaton that is isomorphic to the rightcon
automaton. Weak regular omega-languages reside in the lower levels of the
expressiveness hierarchy of regular omega-languages. Are there more expressive
sub-classes of regular omega languages that have an informative right
congruence? Can we fully characterize the class of languages with a trivial
right congruence? In this paper we try to place some additional pieces of this
big puzzle.
Approximation and Estimation for High-Dimensional Deep Learning Networks
Authors: Andrew R. Barron, Jason M. Klusowski
Date: 2018-09-10 00:00:00
Tasks: Deep Learning, Vocal Bursts Intensity Prediction

It has been experimentally observed in recent years that multi-layer
artificial neural networks have a surprising ability to generalize, even when
trained with far more parameters than observations. Is there a theoretical
basis for this? The best available bounds on their metric entropy and
associated complexity measures are essentially linear in the number of
parameters, which is inadequate to explain this phenomenon. Here we examine the
statistical risk (mean squared predictive error) of multi-layer networks with
$\ell^1$-type controls on their parameters and with ramp activation functions
(also called lower-rectified linear units). In this setting, the risk is shown
to be upper bounded by $[(L^3 \log d)/n]^{1/2}$, where $d$ is the input
dimension to each layer, $L$ is the number of layers, and $n$ is the sample
size. In this way, the input dimension can be much larger than the sample size
and the estimator can still be accurate, provided the target function has such
$\ell^1$ controls and that the sample size is at least moderately large
compared to $L^3\log d$. The heart of the analysis is the development of a
sampling strategy that demonstrates the accuracy of a sparse covering of deep
ramp networks. Lower bounds show that the identified risk is close to being
optimal.
Efficient Counterfactual Learning from Bandit Feedback
Authors: Yusuke Narita, Shota Yasui, Kohei Yata
Date: 2018-09-10 00:00:00
Tasks: Causal Inference, counterfactual, Off-policy evaluation, Visual Object Tracking

What is the most statistically efficient way to do off-policy evaluation and
optimization with batch data from bandit feedback? For log data generated by
contextual bandit algorithms, we consider offline estimators for the expected
reward from a counterfactual policy. Our estimators are shown to have lowest
variance in a wide class of estimators, achieving variance reduction relative
to standard estimators. We then apply our estimators to improve advertisement
design by a major advertisement company. Consistent with the theoretical
result, our estimators allow us to improve on the existing bandit algorithm
with more statistical confidence compared to a state-of-the-art benchmark.
Online Convex Optimization for Sequential Decision Processes and Extensive-Form Games
Authors: Gabriele Farina, Christian Kroer, Tuomas Sandholm
Date: 2018-09-10 00:00:00
Tasks: counterfactual, Decision Making, Form, Sequential Decision Making

Regret minimization is a powerful tool for solving large-scale extensive-form
games. State-of-the-art methods rely on minimizing regret locally at each
decision point. In this work we derive a new framework for regret minimization
on sequential decision problems and extensive-form games with general compact
convex sets at each decision point and general convex losses, as opposed to
prior work which has been for simplex decision points and linear losses. We
call our framework laminar regret decomposition. It generalizes the CFR
algorithm to this more general setting. Furthermore, our framework enables a
new proof of CFR even in the known setting, which is derived from a perspective
of decomposing polytope regret, thereby leading to an arguably simpler
interpretation of the algorithm. Our generalization to convex compact sets and
convex losses allows us to develop new algorithms for several problems:
regularized sequential decision making, regularized Nash equilibria in
extensive-form games, and computing approximate extensive-form perfect
equilibria. Our generalization also leads to the first regret-minimization
algorithm for computing reduced-normal-form quantal response equilibria based
on minimizing local regrets. Experiments show that our framework leads to
algorithms that scale at a rate comparable to the fastest variants of
counterfactual regret minimization for computing Nash equilibrium, and
therefore our approach leads to the first algorithm for computing quantal
response equilibria in extremely large games. Finally we show that our
framework enables a new kind of scalable opponent exploitation approach.
Sample Complexity of Nonparametric Semi-Supervised Learning
Authors: Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
Date: 2018-09-10 00:00:00
Proceeding: NeurIPS 2018
Tasks: Binary Classification, Classification, General Classification, Graph Matching

We study the sample complexity of semi-supervised learning (SSL) and
introduce new assumptions based on the mismatch between a mixture model learned
from unlabeled data and the true mixture model induced by the (unknown) class
conditional distributions. Under these assumptions, we establish an
$\Omega(K\log K)$ labeled sample complexity bound without imposing parametric
assumptions, where $K$ is the number of classes. Our results suggest that even
in nonparametric settings it is possible to learn a near-optimal classifier
using only a few labeled samples. Unlike previous theoretical work which
focuses on binary classification, we consider general multiclass classification
($K>2$), which requires solving a difficult permutation learning problem. This
permutation defines a classifier whose classification error is controlled by
the Wasserstein distance between mixing measures, and we provide finite-sample
results characterizing the behaviour of the excess risk of this classifier.
Finally, we describe three algorithms for computing these estimators based on a
connection to bipartite graph matching, and perform experiments to illustrate
the superiority of the MLE over the majority vote estimator.
A case for deep learning in semantics
Authors: Christopher Potts
Date: 2018-09-10 00:00:00
Tasks: Deep Learning

Pater's target article builds a persuasive case for establishing stronger
ties between theoretical linguistics and connectionism (deep learning). This
commentary extends his arguments to semantics, focusing in particular on issues
of learning, compositionality, and lexical meaning.
Multi-agent online learning in time-varying games
Authors: Benoit Duvocelle, Panayotis Mertikopoulos, Mathias Staudigl, Dries Vermeulen
Date: 2018-09-10 00:00:00

We examine the long-run behavior of multi-agent online learning in games that evolve over time. Specifically, we focus on a wide class of policies based on mirror descent, and we show that the induced sequence of play (a) converges to Nash equilibrium in time-varying games that stabilize in the long run to a strictly monotone limit; and (b) it stays asymptotically close to the evolving equilibrium of the sequence of stage games (assuming they are strongly monotone). Our results apply to both gradient-based and payoff-based feedback - i.e., the "bandit feedback" case where players only get to observe the payoffs of their chosen actions.
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure
Authors: Saeed Mahloujifar, Dimitrios I. Diochnos, Mohammad Mahmoody
Date: 2018-09-09 00:00:00

Many modern machine learning classifiers are shown to be vulnerable to
adversarial perturbations of the instances. Despite a massive amount of work
focusing on making classifiers robust, the task seems quite challenging. In
this work, through a theoretical study, we investigate the adversarial risk and
robustness of classifiers and draw a connection to the well-known phenomenon of
concentration of measure in metric measure spaces. We show that if the metric
probability space of the test instance is concentrated, any classifier with
some initial constant error is inherently vulnerable to adversarial
perturbations.
  One class of concentrated metric probability spaces are the so-called Levy
families that include many natural distributions. In this special case, our
attacks only need to perturb the test instance by at most $O(\sqrt n)$ to make
it misclassified, where $n$ is the data dimension. Using our general result
about Levy instance spaces, we first recover as special case some of the
previously proved results about the existence of adversarial examples. However,
many more Levy families are known (e.g., product distribution under the Hamming
distance) for which we immediately obtain new attacks that find adversarial
examples of distance $O(\sqrt n)$.
  Finally, we show that concentration of measure for product spaces implies the
existence of forms of "poisoning" attacks in which the adversary tampers with
the training data with the goal of degrading the classifier. In particular, we
show that for any learning algorithm that uses $m$ training examples, there is
an adversary who can increase the probability of any "bad property" (e.g.,
failing on a particular test instance) that initially happens with
non-negligible probability to $\approx 1$ by substituting only $\tilde{O}(\sqrt
m)$ of the examples with other (still correctly labeled) examples.