pwc-archive/papers-with-abstracts huggingface dataset:
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
Authors: Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris Papailiopoulos
Date: 2020-07-09 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Fairness, Federated Learning, image-classification, Image Classification, Open-Ended Question Answering, Optical Character Recognition (OCR), Sentiment Analysis

Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in the form of backdoors during training. The goal of a backdoor is to corrupt the performance of the trained model on specific sub-tasks (e.g., by classifying green cars as frogs). A range of FL backdoor attacks have been introduced in the literature, but also methods to defend against them, and it is currently an open question whether FL systems can be tailored to be robust against backdoors. In this work, we provide evidence to the contrary. We first establish that, in the general case, robustness to backdoors implies model robustness to adversarial examples, a major open problem in itself. Furthermore, detecting the presence of a backdoor in a FL model is unlikely assuming first order oracles or polynomial time. We couple our theoretical results with a new family of backdoor attacks, which we refer to as edge-case backdoors. An edge-case backdoor forces a model to misclassify on seemingly easy inputs that are however unlikely to be part of the training, or test data, i.e., they live on the tail of the input distribution. We explain how these edge-case backdoors can lead to unsavory failures and may have serious repercussions on fairness, and exhibit that with careful tuning at the side of the adversary, one can insert them across a range of machine learning tasks (e.g., image classification, OCR, text prediction, sentiment analysis).
Intelligent Warehouse Allocator for Optimal Regional Utilization
Authors: Girish Sathyanarayana, Arun Patro
Date: 2020-07-09 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we describe a novel solution to compute optimal warehouse allocations for fashion inventory. Procured inventory must be optimally allocated to warehouses in proportion to the regional demand around the warehouse. This will ensure that demand is fulfilled by the nearest warehouse thereby minimizing the delivery logistics cost and delivery times. These are key metrics to drive profitability and customer experience respectively. Warehouses have capacity constraints and allocations must minimize inter warehouse redistribution cost of the inventory. This leads to maximum Regional Utilization (RU). We use machine learning and optimization methods to build an efficient solution to this warehouse allocation problem. We use machine learning models to estimate the geographical split of the demand for every product. We use Integer Programming methods to compute the optimal feasible warehouse allocations considering the capacity constraints. We conduct a back-testing by using this solution and validate the efficiency of this model by demonstrating a significant uptick in two key metrics Regional Utilization (RU) and Percentage Two-day-delivery (2DD). We use this process to intelligently create purchase orders with warehouse assignments for Myntra, a leading online fashion retailer.
A Benchmark for Inpainting of Clothing Images with Irregular Holes
Authors: Furkan Kınlı, Barış Özcan, Furkan Kıraç
Date: 2020-07-09 00:00:00
Tasks: Image Inpainting

Fashion image understanding is an active research field with a large number of practical applications for the industry. Despite its practical impacts on intelligent fashion analysis systems, clothing image inpainting has not been extensively examined yet. For that matter, we present an extensive benchmark of clothing image inpainting on well-known fashion datasets. Furthermore, we introduce the use of a dilated version of partial convolutions, which efficiently derive the mask update step, and empirically show that the proposed method reduces the required number of layers to form fully-transparent masks. Experiments show that dilated partial convolutions (DPConv) improve the quantitative inpainting performance when compared to the other inpainting strategies, especially it performs better when the mask size is 20% or more of the image. \keywords{image inpainting, fashion image understanding, dilated convolutions, partial convolutions
Automatic Detection of Major Freeway Congestion Events Using Wireless Traffic Sensor Data: A Machine Learning Approach
Authors: Sanaz Aliari, Kaveh F. Sadabadi
Date: 2020-07-09 00:00:00
Tasks: BIG-bench Machine Learning, Time Series Analysis

Monitoring the dynamics of traffic in major corridors can provide invaluable insight for traffic planning purposes. An important requirement for this monitoring is the availability of methods to automatically detect major traffic events and to annotate the abundance of travel data. This paper introduces a machine learning based approach for reliable detection and characterization of highway traffic congestion events from hundreds of hours of traffic speed data. Indeed, the proposed approach is a generic approach for detection of changes in any given time series, which is the wireless traffic sensor data in the present study. The speed data is initially time-windowed by a ten-hour long sliding window and fed into three Neural Networks that are used to detect the existence and duration of congestion events (slowdowns) in each window. The sliding window captures each slowdown event multiple times and results in increased confidence in congestion detection. The training and parameter tuning are performed on 17,483 hours of data that includes 168 slowdown events. This data is collected and labeled as part of the ongoing probe data validation studies at the Center for Advanced Transportation Technologies (CATT) at the University of Maryland. The Neural networks are carefully trained to reduce the chances of over-fitting to the training data. The experimental results show that this approach is able to successfully detect most of the congestion events, while significantly outperforming a heuristic rule-based approach. Moreover, the proposed approach is shown to be more accurate in estimation of the start-time and end-time of the congestion events.
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces
Authors: Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
Date: 2020-07-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

In this work, we propose KeRNS: an algorithm for episodic reinforcement learning in non-stationary Markov Decision Processes (MDPs) whose state-action set is endowed with a metric. Using a non-parametric model of the MDP built with time-dependent kernels, we prove a regret bound that scales with the covering dimension of the state-action space and the total variation of the MDP with time, which quantifies its level of non-stationarity. Our method generalizes previous approaches based on sliding windows and exponential discounting used to handle changing environments. We further propose a practical implementation of KeRNS, we analyze its regret and validate it experimentally.
Learning dynamical systems from data: a simple cross-validation perspective
Authors: Boumediene Hamzi, Houman Owhadi
Date: 2020-07-09 00:00:00

Regressing the vector field of a dynamical system from a finite number of observed states is a natural way to learn surrogate models for such systems. We present variants of cross-validation (Kernel Flows \cite{Owhadi19} and its variants based on Maximum Mean Discrepancy and Lyapunov exponents) as simple approaches for learning the kernel used in these emulators.
Predictive Value Generalization Bounds
Authors: Keshav Vemuri, Nathan Srebro
Date: 2020-07-09 00:00:00
Tasks: Binary Classification, General Classification, Generalization Bounds

In this paper, we study a bi-criterion framework for assessing scoring functions in the context of binary classification. The positive and negative predictive values (ppv and npv, respectively) are conditional probabilities of the true label matching a classifier's predicted label. The usual classification error rate is a linear combination of these probabilities, and therefore, concentration inequalities for the error rate do not yield confidence intervals for the two separate predictive values. We study generalization properties of scoring functions with respect to predictive values by deriving new distribution-free large deviation and uniform convergence bounds. The latter bound is stated in terms of a measure of function class complexity that we call the order coefficient; we relate this combinatorial quantity to the VC-subgraph dimension.
Program Synthesis with Pragmatic Communication
Authors: Yewen Pu, Kevin Ellis, Marta Kryven, Josh Tenenbaum, Armando Solar-Lezama
Date: 2020-07-09 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Inductive Bias, Program Synthesis

Program synthesis techniques construct or infer programs from user-provided specifications, such as input-output examples. Yet most specifications, especially those given by end-users, leave the synthesis problem radically ill-posed, because many programs may simultaneously satisfy the specification. Prior work resolves this ambiguity by using various inductive biases, such as a preference for simpler programs. This work introduces a new inductive bias derived by modeling the program synthesis task as rational communication, drawing insights from recursive reasoning models of pragmatics. Given a specification, we score a candidate program both on its consistency with the specification, and also whether a rational speaker would chose this particular specification to communicate that program. We develop efficient algorithms for such an approach when learning from input-output examples, and build a pragmatic program synthesizer over a simple grid-like layout domain. A user study finds that end-user participants communicate more effectively with the pragmatic program synthesizer over a non-pragmatic one.
Learning Representations that Support Extrapolation
Authors: Taylor W. Webb, Zachary Dulberg, Steven M. Frankland, Alexander A. Petrov, Randall C. O'Reilly, Jonathan D. Cohen
Date: 2020-07-09 00:00:00
Proceeding: ICML 2020 1

Extrapolation -- the ability to make inferences that go beyond the scope of one's experiences -- is a hallmark of human intelligence. By contrast, the generalization exhibited by contemporary neural network algorithms is largely limited to interpolation between data points in their training corpora. In this paper, we consider the challenge of learning representations that support extrapolation. We introduce a novel visual analogy benchmark that allows the graded evaluation of extrapolation as a function of distance from the convex domain defined by the training data. We also introduce a simple technique, temporal context normalization, that encourages representations that emphasize the relations between objects. We find that this technique enables a significant improvement in the ability to extrapolate, considerably outperforming a number of competitive techniques.
Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers
Authors: Tom Lovett, Mark Briers, Marcos Charalambides, Radka Jersakova, James Lomax, Chris Holmes
Date: 2020-07-09 00:00:00

The Covid-19 pandemic has resulted in a variety of approaches for managing infection outbreaks in international populations. One example is mobile phone applications, which attempt to alert infected individuals and their contacts by automatically inferring two key components of infection risk: the proximity to an individual who may be infected, and the duration of proximity. The former component, proximity, relies on Bluetooth Low Energy (BLE) Received Signal Strength Indicator(RSSI) as a distance sensor, and this has been shown to be problematic; not least because of unpredictable variations caused by different device types, device location on-body, device orientation, the local environment and the general noise associated with radio frequency propagation. In this paper, we present an approach that infers posterior probabilities over distance given sequences of RSSI values. Using a single-dimensional Unscented Kalman Smoother (UKS) for non-linear state space modelling, we outline several Gaussian process observation transforms, including: a generative model that directly captures sources of variation; and a discriminative model that learns a suitable observation function from training data using both distance and infection risk as optimisation objective functions. Our results show that good risk prediction can be achieved in $\mathcal{O}(n)$ time on real-world data sets, with the UKS outperforming more traditional classification methods learned from the same training data.
Multimodal price prediction
Authors: Aidin Zehtab-Salmasi, Ali-Reza Feizi-Derakhshi, Narjes Nikzad-Khasmakhi, Meysam Asgari-Chenaghlu, Saeideh Nabipour
Date: 2020-07-09 00:00:00
Tasks: Multimodal Text and Image Classification, Prediction

Price prediction is one of the examples related to forecasting tasks and is a project based on data science. Price prediction analyzes data and predicts the cost of new products. The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications. So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches. The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations. Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena. The experimental results show 88.3% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques.
SARS-CoV-2 virus RNA sequence classification and geographical analysis with convolutional neural networks approach
Authors: Selcuk Yazar
Date: 2020-07-09 00:00:00

Covid-19 infection, which spread to the whole world in December 2019 and is still active, caused more than 250 thousand deaths in the world today. Researches on this subject have been focused on analyzing the genetic structure of the virus, developing vaccines, the course of the disease, and its source. In this study, RNA sequences belonging to the SARS-CoV-2 virus are transformed into gene motifs with two basic image processing algorithms and classified with the convolutional neural network (CNN) models. The CNN models achieved an average of 98% Area Under Curve(AUC) value was achieved in RNA sequences classified as Asia, Europe, America, and Oceania. The resulting artificial neural network model was used for phylogenetic analysis of the variant of the virus isolated in Turkey. The classification results reached were compared with gene alignment values in the GISAID database, where SARS-CoV-2 virus records are kept all over the world. Our experimental results have revealed that now the detection of the geographic distribution of the virus with the CNN models might serve as an efficient method.
Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data
Authors: Zachary F. Fisher, Younghoon Kim, Barbara Fredrickson, Vladas Pipiras
Date: 2020-07-09 00:00:00
Tasks: Time Series, Time Series Analysis

Intensive Longitudinal Data (ILD) is increasingly available to social and behavioral scientists. With this increased availability come new opportunities for modeling and predicting complex biological, behavioral, and physiological phenomena. Despite these new opportunities psychological researchers have not taken full advantage of promising opportunities inherent to this data, the potential to forecast psychological processes at the individual level. To address this gap in the literature we present a novel modeling framework that addresses a number of topical challenges and open questions in the psychological literature on modeling dynamic processes. First, how can we model and forecast ILD when the length of individual time series and the number of variables collected are roughly equivalent, or when time series lengths are shorter than what is typically required for time series analyses? Second, how can we best take advantage of the cross-sectional (between-person) information inherent to most ILD scenarios while acknowledging individuals differ both quantitatively (e.g. in parameter magnitude) and qualitatively (e.g. in structural dynamics)? Despite the acknowledged between-person heterogeneity in many psychological processes is it possible to leverage group-level information to support improved forecasting at the individual level? In the remainder of the manuscript, we attempt to address these and other pressing questions relevant to the forecasting of multiple-subject ILD.
Advances of Transformer-Based Models for News Headline Generation
Authors: Alexey Bukhtiyarov, Ilya Gusev
Date: 2020-07-09 00:00:00
Tasks: Headline Generation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Understanding, Question Answering, Sentiment Analysis, Text Summarization

Pretrained language models based on Transformer architecture are the reason for recent breakthroughs in many areas of NLP, including sentiment analysis, question answering, named entity recognition. Headline generation is a special kind of text summarization task. Models need to have strong natural language understanding that goes beyond the meaning of individual words and sentences and an ability to distinguish essential information to succeed in it. In this paper, we fine-tune two pretrained Transformer-based models (mBART and BertSumAbs) for that task and achieve new state-of-the-art results on the RIA and Lenta datasets of Russian news. BertSumAbs increases ROUGE on average by 2.9 and 2.0 points respectively over previous best score achieved by Phrase-Based Attentional Transformer and CopyNet.
Behavioral analysis of support vector machine classifier with Gaussian kernel and imbalanced data
Authors: Alaa Tharwat
Date: 2020-07-09 00:00:00
Tasks: Model Selection

The parameters of support vector machines (SVMs) such as the penalty parameter and the kernel parameters have a great impact on the classification accuracy and the complexity of the SVM model. Therefore, the model selection in SVM involves the tuning of these parameters. However, these parameters are usually tuned and used as a black box, without understanding the mathematical background or internal details. In this paper, the behavior of the SVM classification model is analyzed when these parameters take different values with balanced and imbalanced data. This analysis including visualization, mathematical and geometrical interpretations and illustrative numerical examples with the aim of providing the basics of the Gaussian and linear kernel functions with SVM. From this analysis, we proposed a novel search algorithm. In this algorithm, we search for the optimal SVM parameters into two one-dimensional spaces instead of searching into one two-dimensional space. This reduces the computational time significantly. Moreover, in our algorithm, from the analysis of the data, the range of kernel function can be expected. This also reduces the search space and hence reduces the required computational time. Different experiments were conducted to evaluate our search algorithm using different balanced and imbalanced datasets. The results demonstrated how the proposed strategy is fast and effective than other searching strategies.
On the Social and Technical Challenges of Web Search Autosuggestion Moderation
Authors: Timothy J. Hazen, Alexandra Olteanu, Gabriella Kazai, Fernando Diaz, Michael Golebiewski
Date: 2020-07-09 00:00:00

Past research shows that users benefit from systems that support them in their writing and exploration tasks. The autosuggestion feature of Web search engines is an example of such a system: It helps users in formulating their queries by offering a list of suggestions as they type. Autosuggestions are typically generated by machine learning (ML) systems trained on a corpus of search logs and document representations. Such automated methods can become prone to issues that result in problematic suggestions that are biased, racist, sexist or in other ways inappropriate. While current search engines have become increasingly proficient at suppressing such problematic suggestions, there are still persistent issues that remain. In this paper, we reflect on past efforts and on why certain issues still linger by covering explored solutions along a prototypical pipeline for identifying, detecting, and addressing problematic autosuggestions. To showcase their complexity, we discuss several dimensions of problematic suggestions, difficult issues along the pipeline, and why our discussion applies to the increasing number of applications beyond web search that implement similar textual suggestion features. By outlining persistent social and technical challenges in moderating web search suggestions, we provide a renewed call for action.
The Mean-Squared Error of Double Q-Learning
Authors: Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant
Date: 2020-07-09 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Q-Learning

In this paper, we establish a theoretical comparison between the asymptotic mean-squared error of Double Q-learning and Q-learning. Our result builds upon an analysis for linear stochastic approximation based on Lyapunov equations and applies to both tabular setting and with linear function approximation, provided that the optimal policy is unique and the algorithms converge. We show that the asymptotic mean-squared error of Double Q-learning is exactly equal to that of Q-learning if Double Q-learning uses twice the learning rate of Q-learning and outputs the average of its two estimators. We also present some practical implications of this theoretical observation using simulations.
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models
Authors: Adarsh K. Jeewajee, Leslie P. Kaelbling
Date: 2020-07-09 00:00:00
Proceeding: NeurIPS 2020 12

Undirected graphical models are compact representations of joint probability distributions over random variables. To solve inference tasks of interest, graphical models of arbitrary topology can be trained using empirical risk minimization. However, to solve inference tasks that were not seen during training, these models (EGMs) often need to be re-trained. Instead, we propose an inference-agnostic adversarial training framework which produces an infinitely-large ensemble of graphical models (AGMs). The ensemble is optimized to generate data within the GAN framework, and inference is performed using a finite subset of these models. AGMs perform comparably with EGMs on inference tasks that the latter were specifically optimized for. Most importantly, AGMs show significantly better generalization to unseen inference tasks compared to EGMs, as well as deep neural architectures like GibbsNet and VAEAC which allow arbitrary conditioning. Finally, AGMs allow fast data sampling, competitive with Gibbs sampling from EGMs.