pwc-archive/papers-with-abstracts huggingface dataset:
Autonomous Highway Driving using Deep Reinforcement Learning
Authors: Subramanya Nageshrao, Eric Tseng, Dimitar Filev
Date: 2019-03-29 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The operational space of an autonomous vehicle (AV) can be diverse and vary
significantly. This may lead to a scenario that was not postulated in the
design phase. Due to this, formulating a rule based decision maker for
selecting maneuvers may not be ideal. Similarly, it may not be effective to
design an a-priori cost function and then solve the optimal control problem in
real-time. In order to address these issues and to avoid peculiar behaviors
when encountering unforeseen scenario, we propose a reinforcement learning (RL)
based method, where the ego car, i.e., an autonomous vehicle, learns to make
decisions by directly interacting with simulated traffic. The decision maker
for AV is implemented as a deep neural network providing an action choice for a
given system state. In a critical application such as driving, an RL agent
without explicit notion of safety may not converge or it may need extremely
large number of samples before finding a reliable policy. To best address the
issue, this paper incorporates reinforcement learning with an additional short
horizon safety check (SC). In a critical scenario, the safety check will also
provide an alternate safe action to the agent provided if it exists. This leads
to two novel contributions. First, it generalizes the states that could lead to
undesirable "near-misses" or "collisions ". Second, inclusion of safety check
can provide a safe and stable training environment. This significantly enhances
learning efficiency without inhibiting meaningful exploration to ensure safe
and optimal learned behavior. We demonstrate the performance of the developed
algorithm in highway driving scenario where the trained AV encounters varying
traffic density in a highway setting.
Interpreting Black Box Models via Hypothesis Testing
Authors: Collin Burns, Jesse Thomason, Wesley Tansey
Date: 2019-03-29 00:00:00
Tasks: Two-sample testing

In science and medicine, model interpretations may be reported as discoveries of natural phenomena or used to guide patient treatments. In such high-stakes tasks, false discoveries may lead investigators astray. These applications would therefore benefit from control over the finite-sample error rate of interpretations. We reframe black box model interpretability as a multiple hypothesis testing problem. The task is to discover "important" features by testing whether the model prediction is significantly different from what would be expected if the features were replaced with uninformative counterfactuals. We propose two testing methods: one that provably controls the false discovery rate but which is not yet feasible for large-scale applications, and an approximate testing method which can be applied to real-world data sets. In simulation, both tests have high power relative to existing interpretability methods. When applied to state-of-the-art vision and language models, the framework selects features that intuitively explain model predictions. The resulting explanations have the additional advantage that they are themselves easy to interpret.
Statistical learnability of nuclear masses
Authors: Andrea Idini
Date: 2019-03-29 00:00:00

After more than 80 years from the seminal work of Weizs\"acker and the liquid drop model of the atomic nucleus, deviations from experiments of mass models ($\sim$ MeV) are orders of magnitude larger than experimental errors ($\lesssim$ keV). Predicting the mass of atomic nuclei with precision is extremely challenging. This is due to the non--trivial many--body interplay of protons and neutrons in nuclei, and the complex nature of the nuclear strong force. Statistical theory of learning will be used to provide bounds to the prediction errors of model trained with a finite data set. These bounds are validated with neural network calculations, and compared with state of the art mass models. Therefore, it will be argued that the nuclear structure models investigating ground state properties explore a system on the limit of the knowledgeable, as defined by the statistical theory of learning.
Data Amplification: A Unified and Competitive Approach to Property Estimation
Authors: Yi Hao, Alon Orlitsky, Ananda T. Suresh, Yihong Wu
Date: 2019-03-29 00:00:00
Proceeding: NeurIPS 2018 12

Estimating properties of discrete distributions is a fundamental problem in
statistical learning. We design the first unified, linear-time, competitive,
property estimator that for a wide class of properties and for all underlying
distributions uses just $2n$ samples to achieve the performance attained by the
empirical estimator with $n\sqrt{\log n}$ samples. This provides off-the-shelf,
distribution-independent, "amplification" of the amount of data available
relative to common-practice estimators.
  We illustrate the estimator's practical advantages by comparing it to
existing estimators for a wide variety of properties and distributions. In most
cases, its performance with $n$ samples is even as good as that of the
empirical estimator with $n\log n$ samples, and for essentially all properties,
its performance is comparable to that of the best existing estimator designed
specifically for that property.
ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT
Authors: Chenyang Huang, Amine Trabelsi, Osmar R. Zaïane
Date: 2019-03-30 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Emotion Recognition in Conversation, General Classification, text-classification, Text Classification

This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchical LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational context. The results show that, in this task, our HRCLE outperforms the most recent state-of-the-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.
On Arrhythmia Detection by Deep Learning and Multidimensional Representation
Authors: K. S. Rajput, S. Wibowo, C. Hao, M. Majmudar
Date: 2019-03-30 00:00:00
Tasks: Arrhythmia Detection, Deep Learning, Electrocardiography (ECG), General Classification, Time Series, Time Series Analysis

An electrocardiogram (ECG) is a time-series signal that is represented by
one-dimensional (1-D) data. Higher dimensional representation contains more
information that is accessible for feature extraction. Hidden variables such as
frequency relation and morphology of segment is not directly accessible in the
time domain. In this paper, 1-D time series data is converted into
multi-dimensional representation in the form of multichannel 2-D images.
Following that, deep learning was used to train a deep neural network based
classifier to detect arrhythmias. The results of simulation on testing database
demonstrate the effectiveness of the proposed methodology by showing an
outstanding classification performance compared to other existing methods and
hand-crafted annotations made by certified cardiologists.
Learning Affective Correspondence between Music and Image
Authors: Gaurav Verma, Eeshan Gunesh Dhekane, Tanaya Guha
Date: 2019-03-30 00:00:00
Tasks: Binary Classification, Emotion Recognition

We introduce the problem of learning affective correspondence between audio
(music) and visual data (images). For this task, a music clip and an image are
considered similar (having true correspondence) if they have similar emotion
content. In order to estimate this crossmodal, emotion-centric similarity, we
propose a deep neural network architecture that learns to project the data from
the two modalities to a common representation space, and performs a binary
classification task of predicting the affective correspondence (true or false).
To facilitate the current study, we construct a large scale database containing
more than $3,500$ music clips and $85,000$ images with three emotion classes
(positive, neutral, negative). The proposed approach achieves $61.67\%$
accuracy for the affective correspondence prediction task on this database,
outperforming two relevant and competitive baselines. We also demonstrate that
our network learns modality-specific representations of emotion (without
explicitly being trained with emotion labels), which are useful for emotion
recognition in individual modalities.
Machine translation considering context information using Encoder-Decoder model
Authors: Tetsuto Takano, Satoshi Yamane
Date: 2019-03-30 00:00:00
Tasks: Decoder, Machine Translation, Sentence, Translation

In the task of machine translation, context information is one of the
important factor. But considering the context information model dose not
proposed. The paper propose a new model which can integrate context information
and make translation. In this paper, we create a new model based Encoder
Decoder model. When translating current sentence, the model integrates output
from preceding encoder with current encoder. The model can consider context
information and the result score is higher than existing model.
Asymptotic nonparametric statistical analysis of stationary time series
Authors: Daniil Ryabko
Date: 2019-03-30 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Stationarity is a very general, qualitative assumption, that can be assessed
on the basis of application specifics. It is thus a rather attractive
assumption to base statistical analysis on, especially for problems for which
less general qualitative assumptions, such as independence or finite memory,
clearly fail. However, it has long been considered too general to allow for
statistical inference to be made. One of the reasons for this is that rates of
convergence, even of frequencies to the mean, are not available under this
assumption alone. Recently, it has been shown that, while some natural and
simple problems such as homogeneity, are indeed provably impossible to solve if
one only assumes that the data is stationary (or stationary ergodic), many
others can be solved using rather simple and intuitive algorithms. The latter
problems include clustering and change point estimation. In this volume I
summarize these results. The emphasis is on asymptotic consistency, since this
the strongest property one can obtain assuming stationarity alone. While for
most of the problems for which a solution is found this solution is
algorithmically realizable, the main objective in this area of research, the
objective which is only partially attained, is to understand what is possible
and what is not possible to do for stationary time series. The considered
problems include homogeneity testing, clustering with respect to distribution,
clustering with respect to independence, change-point estimation, identity
testing, and the general question of composite hypotheses testing. For the
latter problem, a topological criterion for the existence of a consistent test
is presented. In addition, several open questions are discussed.
Nonparametric Density Estimation for High-Dimensional Data - Algorithms and Applications
Authors: Zhipeng Wang, David W. Scott
Date: 2019-03-30 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Density Estimation, Vocal Bursts Intensity Prediction

Density Estimation is one of the central areas of statistics whose purpose is
to estimate the probability density function underlying the observed data. It
serves as a building block for many tasks in statistical inference,
visualization, and machine learning. Density Estimation is widely adopted in
the domain of unsupervised learning especially for the application of
clustering. As big data become pervasive in almost every area of data sciences,
analyzing high-dimensional data that have many features and variables appears
to be a major focus in both academia and industry. High-dimensional data pose
challenges not only from the theoretical aspects of statistical inference, but
also from the algorithmic/computational considerations of machine learning and
data analytics. This paper reviews a collection of selected nonparametric
density estimation algorithms for high-dimensional data, some of them are
recently published and provide interesting mathematical insights. The important
application domain of nonparametric density estimation, such as { modal
clustering}, are also included in this paper. Several research directions
related to density estimation and high-dimensional data analysis are suggested
by the authors.
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits
Authors: Yingkai Li, Yining Wang, Yuan Zhou
Date: 2019-03-30 00:00:00
Tasks: Multi-Armed Bandits

We study the linear contextual bandit problem with finite action sets. When the problem dimension is $d$, the time horizon is $T$, and there are $n \leq 2^{d/2}$ candidate actions per time period, we (1) show that the minimax expected regret is $\Omega(\sqrt{dT (\log T) (\log n)})$ for every algorithm, and (2) introduce a Variable-Confidence-Level (VCL) SupLinUCB algorithm whose regret matches the lower bound up to iterated logarithmic factors. Our algorithmic result saves two $\sqrt{\log T}$ factors from previous analysis, and our information-theoretical lower bound also improves previous results by one $\sqrt{\log T}$ factor, revealing a regret scaling quite different from classical multi-armed bandits in which no logarithmic $T$ term is present in minimax regret. Our proof techniques include variable confidence levels and a careful analysis of layer sizes of SupLinUCB on the upper bound side, and delicately constructed adversarial sequences showing the tightness of elliptical potential lemmas on the lower bound side.
Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning
Authors: Chuang Ye, M. Cenk Gursoy, Senem Velipasalar
Date: 2019-03-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we investigate the problem of power control for streaming
variable bit rate (VBR) videos over wireless links. A system model involving a
transmitter (e.g., a base station) that sends VBR video data to a receiver
(e.g., a mobile user) equipped with a playout buffer is adopted, as used in
dynamic adaptive streaming video applications. In this setting, we analyze
power control policies considering the following two objectives: 1) the
minimization of the transmit power consumption, and 2) the minimization of the
transmission completion time of the communication session. In order to play the
video without interruptions, the power control policy should also satisfy the
requirement that the VBR video data is delivered to the mobile user without
causing playout buffer underflow or overflows. A directional water-filling
algorithm, which provides a simple and concise interpretation of the necessary
optimality conditions, is identified as the optimal offline policy. Following
this, two online policies are proposed for power control based on channel side
information (CSI) prediction within a short time window. Dynamic programming is
employed to implement the optimal offline and the initial online power control
policies that minimize the transmit power consumption in the communication
session. Subsequently, reinforcement learning (RL) based approach is employed
for the second online power control policy. Via simulation results, we show
that the optimal offline power control policy that minimizes the overall power
consumption leads to substantial energy savings compared to the strategy of
minimizing the time duration of video streaming. We also demonstrate that the
RL algorithm performs better than the dynamic programming based online grouped
water-filling (GWF) strategy unless the channel is highly correlated.
Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues
Authors: Sungjoon Park, Donghyun Kim, Alice Oh
Date: 2019-03-31 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Modeling, Language Modelling

The recent surge of text-based online counseling applications enables us to
collect and analyze interactions between counselors and clients. A dataset of
those interactions can be used to learn to automatically classify the client
utterances into categories that help counselors in diagnosing client status and
predicting counseling outcome. With proper anonymization, we collect
counselor-client dialogues, define meaningful categories of client utterances
with professional counselors, and develop a novel neural network model for
classifying the client utterances. The central idea of our model, ConvMFiT, is
a pre-trained conversation model which consists of a general language model
built from an out-of-domain corpus and two role-specific language models built
from unlabeled in-domain dialogues. The classification result shows that
ConvMFiT outperforms state-of-the-art comparison models. Further, the attention
weights in the learned model confirm that the model finds expected linguistic
patterns for each category.
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs
Authors: Gitta Kutyniok, Philipp Petersen, Mones Raslan, Reinhold Schneider
Date: 2019-03-31 00:00:00

We derive upper bounds on the complexity of ReLU neural networks approximating the solution maps of parametric partial differential equations. In particular, without any knowledge of its concrete shape, we use the inherent low-dimensionality of the solution manifold to obtain approximation rates which are significantly superior to those provided by classical neural network approximation results. Concretely, we use the existence of a small reduced basis to construct, for a large variety of parametric partial differential equations, neural networks that yield approximations of the parametric solution maps in such a way that the sizes of these networks essentially only depend on the size of the reduced basis.
Perturbative estimation of stochastic gradients
Authors: Luca Ambrogioni, Marcel A. J. van Gerven
Date: 2019-03-31 00:00:00
Tasks: Variational Inference

In this paper we introduce a family of stochastic gradient estimation techniques based of the perturbative expansion around the mean of the sampling distribution. We characterize the bias and variance of the resulting Taylor-corrected estimators using the Lagrange error formula. Furthermore, we introduce a family of variance reduction techniques that can be applied to other gradient estimators. Finally, we show that these new perturbative methods can be extended to discrete functions using analytic continuation. Using this technique, we derive a new gradient descent method for training stochastic networks with binary weights. In our experiments, we show that the perturbative correction improves the convergence of stochastic variational inference both in the continuous and in the discrete case.
Sparse Tensor Additive Regression
Authors: Botao Hao, Boxiang Wang, Pengyuan Wang, Jingfei Zhang, Jian Yang, Will Wei Sun
Date: 2019-03-31 00:00:00
Tasks: Click-Through Rate Prediction, Marketing, parameter estimation, regression

Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing. In this paper, we propose a sparse tensor additive regression (STAR) that models a scalar response as a flexible nonparametric function of tensor covariates. The proposed model effectively exploits the sparse and low-rank structures in the tensor additive regression. We formulate the parameter estimation as a non-convex optimization problem, and propose an efficient penalized alternating minimization algorithm. We establish a non-asymptotic error bound for the estimator obtained from each iteration of the proposed algorithm, which reveals an interplay between the optimization error and the statistical rate of convergence. We demonstrate the efficacy of STAR through extensive comparative simulation studies, and an application to the click-through-rate prediction in online advertising.
Semisupervised Clustering by Queries and Locally Encodable Source Coding
Authors: Arya Mazumdar, Soumyabrata Pal
Date: 2019-03-31 00:00:00
Tasks: Clustering, Data Compression

Source coding is the canonical problem of data compression in information theory. In a locally encodable source coding, each compressed bit depends on only few bits of the input. In this paper, we show that a recently popular model of semi-supervised clustering is equivalent to locally encodable source coding. In this model, the task is to perform multiclass labeling of unlabeled elements. At the beginning, we can ask in parallel a set of simple queries to an oracle who provides (possibly erroneous) binary answers to the queries. The queries cannot involve more than two (or a fixed constant number of) elements. Now the labeling of all the elements (or clustering) must be performed based on the noisy query answers. The goal is to recover all the correct labelings while minimizing the number of such queries. The equivalence to locally encodable source codes leads us to find lower bounds on the number of queries required in a variety of scenarios. We provide querying schemes based on pairwise `same cluster' queries - and pairwise AND queries and show provable performance guarantees for each of the schemes.
Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models
Authors: Yaniv Shulman
Date: 2019-04-01 00:00:00
Tasks: Anomaly Detection, Contextual Anomaly Detection, Unsupervised Anomaly Detection, Unsupervised Contextual Anomaly Detection

A method for unsupervised contextual anomaly detection is proposed using a
cross-linked pair of Variational Auto-Encoders for assigning a normality score
to an observation. The method enables a distinct separation of contextual from
behavioral attributes and is robust to the presence of anomalous or novel
contextual attributes. The method can be trained with data sets that contain
anomalies without any special pre-processing.
Robust Optimisation Monte Carlo
Authors: Borislav Ikonomov, Michael U. Gutmann
Date: 2019-04-01 00:00:00
Tasks: Bayesian Inference

This paper is on Bayesian inference for parametric statistical models that are defined by a stochastic simulator which specifies how data is generated. Exact sampling is then possible but evaluating the likelihood function is typically prohibitively expensive. Approximate Bayesian Computation (ABC) is a framework to perform approximate inference in such situations. While basic ABC algorithms are widely applicable, they are notoriously slow and much research has focused on increasing their efficiency. Optimisation Monte Carlo (OMC) has recently been proposed as an efficient and embarrassingly parallel method that leverages optimisation to accelerate the inference. In this paper, we demonstrate an important previously unrecognised failure mode of OMC: It generates strongly overconfident approximations by collapsing regions of similar or near-constant likelihood into a single point. We propose an efficient, robust generalisation of OMC that corrects this. It makes fewer assumptions, retains the main benefits of OMC, and can be performed either as post-processing to OMC or as a stand-alone computation. We demonstrate the effectiveness of the proposed Robust OMC on toy examples and tasks in inverse-graphics where we perform Bayesian inference with a complex image renderer.