pwc-archive/papers-with-abstracts huggingface dataset:
Differentially Private Quantiles
Authors: Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
Date: 2021-02-16 00:00:00

Quantiles are often used for summarizing and understanding data. If that data is sensitive, it may be necessary to compute quantiles in a way that is differentially private, providing theoretical guarantees that the result does not reveal private information. However, when multiple quantiles are needed, existing differentially private algorithms fare poorly: they either compute quantiles individually, splitting the privacy budget, or summarize the entire distribution, wasting effort. In either case the result is reduced accuracy. In this work we propose an instance of the exponential mechanism that simultaneously estimates exactly $m$ quantiles from $n$ data points while guaranteeing differential privacy. The utility function is carefully structured to allow for an efficient implementation that returns estimates of all $m$ quantiles in time $O(mn\log(n) + m^2n)$. Experiments show that our method significantly outperforms the current state of the art on both real and synthetic data while remaining efficient enough to be practical.
Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models
Authors: Zixuan Liu, Ehsan Adeli, Kilian M. Pohl, Qingyu Zhao
Date: 2021-02-16 00:00:00
Tasks: Image-to-Image Translation

Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision process of a trained classifier, existing techniques typically rely on saliency maps to quantify the voxel-wise or feature-level importance for classification through partial derivatives. Despite providing some level of localization, these maps are not human-understandable from the neuroscience perspective as they do not inform the specific meaning of the alteration linked to the brain disorder. Inspired by the image-to-image translation scheme, we propose to train simulator networks that can warp a given image to inject or remove patterns of the disease. These networks are trained such that the classifier produces consistently increased or decreased prediction logits for the simulated images. Moreover, we propose to couple all the simulators into a unified model based on conditional convolution. We applied our approach to interpreting classifiers trained on a synthetic dataset and two neuroimaging datasets to visualize the effect of the Alzheimer's disease and alcohol use disorder. Compared to the saliency maps generated by baseline approaches, our simulations and visualizations based on the Jacobian determinants of the warping field reveal meaningful and understandable patterns related to the diseases.
The MELODIC family for simultaneous binary logistic regression in a reduced space
Authors: Mark de Rooij, Patrick J. F. Groenen
Date: 2021-02-16 00:00:00
Tasks: Binary Classification, parameter estimation, regression

Logistic regression is a commonly used method for binary classification. Researchers often have more than a single binary response variable and simultaneous analysis is beneficial because it provides insight into the dependencies among response variables as well as between the predictor variables and the responses. Moreover, in such a simultaneous analysis the equations can lend each other strength, which might increase predictive accuracy. In this paper, we propose the MELODIC family for simultaneous binary logistic regression modeling. In this family, the regression models are defined in a Euclidean space of reduced dimension, based on a distance rule. The model may be interpreted in terms of logistic regression coefficients or in terms of a biplot. We discuss a fast iterative majorization (or MM) algorithm for parameter estimation. Two applications are shown in detail: one relating personality characteristics to drug consumption profiles and one relating personality characteristics to depressive and anxiety disorders. We present a thorough comparison of our MELODIC family with alternative approaches for multivariate binary data.
Non-Autoregressive Text Generation with Pre-trained Language Models
Authors: Yixuan Su, Deng Cai, Yan Wang, David Vandyke, Simon Baker, Piji Li, Nigel Collier
Date: 2021-02-16 00:00:00
Proceeding: EACL 2021 2
Tasks: Machine Translation, Sentence, Sentence Compression, Text Generation, Text Summarization, Translation

Non-autoregressive generation (NAG) has recently attracted great attention due to its fast inference speed. However, the generation quality of existing NAG models still lags behind their autoregressive counterparts. In this work, we show that BERT can be employed as the backbone of a NAG model to greatly improve performance. Additionally, we devise mechanisms to alleviate the two common problems of vanilla NAG models: the inflexibility of prefixed output length and the conditional independence of individual token predictions. Lastly, to further increase the speed advantage of the proposed model, we propose a new decoding strategy, ratio-first, for applications where the output lengths can be approximately estimated beforehand. For a comprehensive evaluation, we test the proposed model on three text generation tasks, including text summarization, sentence compression and machine translation. Experimental results show that our model significantly outperforms existing non-autoregressive baselines and achieves competitive performance with many strong autoregressive models. In addition, we also conduct extensive analysis experiments to reveal the effect of each proposed component.
Unifying Lower Bounds on Prediction Dimension of Consistent Convex Surrogates
Authors: Jessie Finocchiaro, Rafael Frongillo, Bo Waggoner
Date: 2021-02-16 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Prediction, Structured Prediction

Given a prediction task, understanding when one can and cannot design a consistent convex surrogate loss, particularly a low-dimensional one, is an important and active area of machine learning research. The prediction task may be given as a target loss, as in classification and structured prediction, or simply as a (conditional) statistic of the data, as in risk measure estimation. These two scenarios typically involve different techniques for designing and analyzing surrogate losses. We unify these settings using tools from property elicitation, and give a general lower bound on prediction dimension. Our lower bound tightens existing results in the case of discrete predictions, showing that previous calibration-based bounds can largely be recovered via property elicitation. For continuous estimation, our lower bound resolves on open problem on estimating measures of risk and uncertainty.
The Yin-Yang dataset
Authors: Laura Kriener, Julian Göltz, Mihai A. Petrovici
Date: 2021-02-16 00:00:00
Tasks: Deep Learning

The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks. It serves as an alternative to classic deep learning datasets, especially in early-stage prototyping scenarios for both network models and hardware platforms, for which it provides several advantages. First, it is smaller and therefore faster to learn, thereby being better suited for small-scale exploratory studies in both software simulations and hardware prototypes. Second, it exhibits a very clear gap between the accuracies achievable using shallow as compared to deep neural networks. Third, it is easily transferable between spatial and temporal input domains, making it interesting for different types of classification scenarios.
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
Authors: Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
Date: 2021-02-16 00:00:00
Tasks: regression

We propose to analyse the conditional distributional treatment effect (CoDiTE), which, in contrast to the more common conditional average treatment effect (CATE), is designed to encode a treatment's distributional aspects beyond the mean. We first introduce a formal definition of the CoDiTE associated with a distance function between probability measures. Then we discuss the CoDiTE associated with the maximum mean discrepancy via kernel conditional mean embeddings, which, coupled with a hypothesis test, tells us whether there is any conditional distributional effect of the treatment. Finally, we investigate what kind of conditional distributional effect the treatment has, both in an exploratory manner via the conditional witness function, and in a quantitative manner via U-statistic regression, generalising the CATE to higher-order moments. Experiments on synthetic, semi-synthetic and real datasets demonstrate the merits of our approach.
Improper Reinforcement Learning with Gradient-based Policy Optimization
Authors: Mohammadi Zaki, Avinash Mohan, Aditya Gopalan, Shie Mannor
Date: 2021-02-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider an improper reinforcement learning setting where a learner is given $M$ base controllers for an unknown Markov decision process, and wishes to combine them optimally to produce a potentially new controller that can outperform each of the base ones. This can be useful in tuning across controllers, learnt possibly in mismatched or simulated environments, to obtain a good controller for a given target environment with relatively few trials. \par We propose a gradient-based approach that operates over a class of improper mixtures of the controllers. We derive convergence rate guarantees for the approach assuming access to a gradient oracle. The value function of the mixture and its gradient may not be available in closed-form; however, we show that we can employ rollouts and simultaneous perturbation stochastic approximation (SPSA) for explicit gradient descent optimization. Numerical results on (i) the standard control theoretic benchmark of stabilizing an inverted pendulum and (ii) a constrained queueing task show that our improper policy optimization algorithm can stabilize the system even when the base policies at its disposal are unstable\footnote{Under review. Please do not distribute.}.
Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning
Authors: Jakob Greis, Artem Yushchenko, Daniel Vogel, Michael Meier, Volker Steinhage
Date: 2021-02-16 00:00:00
Tasks: Deep Learning

Many IoT devices are vulnerable to attacks due to flawed security designs and lacking mechanisms for firmware updates or patches to eliminate the security vulnerabilities. Device-type identification combined with data from vulnerability databases can pinpoint vulnerable IoT devices in a network and can be used to constrain the communications of vulnerable devices for preventing damage. In this contribution, we present and evaluate two deep learning approaches to the reliable IoT device-type identification, namely a recurrent and a convolutional network architecture. Both deep learning approaches show accuracies of 97% and 98%, respectively, and thereby outperform an up-to-date IoT device-type identification approach using hand-crafted fingerprint features obtaining an accuracy of 82%. The runtime performance for the IoT identification of both deep learning approaches outperforms the hand-crafted approach by three magnitudes. Finally, importance metrics explain the results of both deep learning approaches in terms of the utilization of the analyzed traffic data flow.
Constructing Multiclass Classifiers using Binary Classifiers Under Log-Loss
Authors: Assaf Ben-Yishai, Or Ordentlich
Date: 2021-02-16 00:00:00
Tasks: Classification

The construction of multiclass classifiers from binary elements is studied in this paper, and performance is quantified by the regret, defined with respect to the Bayes optimal log-loss. We discuss two known methods. The first is one vs. all (OVA), for which we prove that the multiclass regret is upper bounded by the sum of binary regrets of the constituent classifiers. The second is hierarchical classification, based on a binary tree. For this method we prove that the multiclass regret is exactly a weighted sum of constituent binary regrets where the weighing is determined by the tree structure. We also introduce a leverage-hierarchical classification method, which potentially yields smaller log-loss and regret. The advantages of these classification methods are demonstrated by simulation on both synthetic and real-life datasets.
Comparison of semi-supervised deep learning algorithms for audio classification
Authors: Léo Cances, Etienne Labbé, Thomas Pellegrini
Date: 2021-02-16 00:00:00
Tasks: Audio Classification, Audio Tagging, Data Augmentation, Deep Learning, Environmental Sound Classification, Object Recognition, Sound Classification

In this article, we adapted five recent SSL methods to the task of audio classification. The first two methods, namely Deep Co-Training (DCT) and Mean Teacher (MT), involve two collaborative neural networks. The three other algorithms, called MixMatch (MM), ReMixMatch (RMM), and FixMatch (FM), are single-model methods that rely primarily on data augmentation strategies. Using the Wide-ResNet-28-2 architecture in all our experiments, 10% of labeled data and the remaining 90% as unlabeled data for training, we first compare the error rates of the five methods on three standard benchmark audio datasets: Environmental Sound Classification (ESC-10), UrbanSound8K (UBS8K), and Google Speech Commands (GSC). In all but one cases, MM, RMM, and FM outperformed MT and DCT significantly, MM and RMM being the best methods in most experiments. On UBS8K and GSC, MM achieved 18.02% and 3.25% error rate (ER), respectively, outperforming models trained with 100% of the available labeled data, which reached 23.29% and 4.94%, respectively. RMM achieved the best results on ESC-10 (12.00% ER), followed by FM which reached 13.33%. Second, we explored adding the mixup augmentation, used in MM and RMM, to DCT, MT, and FM. In almost all cases, mixup brought consistent gains. For instance, on GSC, FM reached 4.44% and 3.31% ER without and with mixup. Our PyTorch code will be made available upon paper acceptance at https:// github. com/ Labbe ti/ SSLH.
Value of Information for Argumentation based Intelligence Analysis
Authors: Todd Robinson
Date: 2021-02-16 00:00:00
Tasks: Decision Making

Argumentation provides a representation of arguments and attacks between these arguments. Argumentation can be used to represent a reasoning process over evidence to reach conclusions. Within such a reasoning process, understanding the value of information can improve the quality of decision making based on the output of the reasoning process. The value of an item of information is inherently dependent on the available evidence and the question being answered by the reasoning. In this paper we introduce a value of information on argument frameworks to identify the most valuable arguments within the finite set of arguments in the framework, and the arguments and attacks which could be added to change the output of an evaluation. We demonstrate the value of information within an argument framework representing an intelligence analysis in the maritime domain. Understanding the value of information in an intelligence analysis will allow analysts to balance the value against the costs and risks of collection, to effectively request further collection of intelligence to increase the confidence in the analysis of hypotheses.
Accurate and Clear Precipitation Nowcasting with Consecutive Attention and Rain-map Discrimination
Authors: Ashesh, Buo-Fu Chen, Treng-Shi Huang, Boyo Chen, Chia-Tung Chang, Hsuan-Tien Lin
Date: 2021-02-16 00:00:00
Tasks: Weather Forecasting

Precipitation nowcasting is an important task for weather forecasting. Many recent works aim to predict the high rainfall events more accurately with the help of deep learning techniques, but such events are relatively rare. The rarity is often addressed by formulations that re-weight the rare events. Somehow such a formulation carries a side effect of making "blurry" predictions in low rainfall regions and cannot convince meteorologists to trust its practical usability. We fix the trust issue by introducing a discriminator that encourages the prediction model to generate realistic rain-maps without sacrificing predictive accuracy. Furthermore, we extend the nowcasting time frame from one hour to three hours to further address the needs from meteorologists. The extension is based on consecutive attentions across different hours. We propose a new deep learning model for precipitation nowcasting that includes both the discrimination and attention techniques. The model is examined on a newly-built benchmark dataset that contains both radar data and actual rain data. The benchmark, which will be publicly released, not only establishes the superiority of the proposed model, but also is expected to encourage future research on precipitation nowcasting.
Just Noticeable Difference for Deep Machine Vision
Authors: Jian Jin, Xingxing Zhang, Xin Fu, huan zhang, Weisi Lin, Jian Lou, Yao Zhao
Date: 2021-02-16 00:00:00
Tasks: image-classification, Image Classification, Neural Network Security, Video Compression

As an important perceptual characteristic of the Human Visual System (HVS), the Just Noticeable Difference (JND) has been studied for decades with image and video processing (e.g., perceptual visual signal compression). However, there is little exploration on the existence of JND for the Deep Machine Vision (DMV), although the DMV has made great strides in many machine vision tasks. In this paper, we take an initial attempt, and demonstrate that the DMV has the JND, termed as the DMV-JND. We then propose a JND model for the image classification task in the DMV. It has been discovered that the DMV can tolerate distorted images with average PSNR of only 9.56dB (the lower the better), by generating JND via unsupervised learning with the proposed DMV-JND-NET. In particular, a semantic-guided redundancy assessment strategy is designed to restrain the magnitude and spatial distribution of the DMV-JND. Experimental results on image classification demonstrate that we successfully find the JND for deep machine vision. Our DMV-JND facilitates a possible direction for DMV-oriented image and video compression, watermarking, quality assessment, deep neural network security, and so on.
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
Authors: Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, Sébastien Rouault, John Stephan
Date: 2021-02-16 00:00:00

This paper addresses the problem of combining Byzantine resilience with privacy in machine learning (ML). Specifically, we study if a distributed implementation of the renowned Stochastic Gradient Descent (SGD) learning algorithm is feasible with both differential privacy (DP) and $(\alpha,f)$-Byzantine resilience. To the best of our knowledge, this is the first work to tackle this problem from a theoretical point of view. A key finding of our analyses is that the classical approaches to these two (seemingly) orthogonal issues are incompatible. More precisely, we show that a direct composition of these techniques makes the guarantees of the resulting SGD algorithm depend unfavourably upon the number of parameters of the ML model, making the training of large models practically infeasible. We validate our theoretical results through numerical experiments on publicly-available datasets; showing that it is impractical to ensure DP and Byzantine resilience simultaneously.
Integrating Floor Plans into Hedonic Models for Rent Price Appraisal
Authors: Kirill Solovev, Nicolas Pröllochs
Date: 2021-02-16 00:00:00

Online real estate platforms have become significant marketplaces facilitating users' search for an apartment or a house. Yet it remains challenging to accurately appraise a property's value. Prior works have primarily studied real estate valuation based on hedonic price models that take structured data into account while accompanying unstructured data is typically ignored. In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal. We propose a tailored two-staged deep learning approach to learn price-relevant designs of floor plans from historical price data. Subsequently, we integrate the floor plan predictions into hedonic rent price models that account for both structural and locational characteristics of an apartment. Our empirical analysis based on a unique dataset of 9174 real estate listings suggests that current hedonic models underutilize the available data. We find that (1) the visual design of floor plans has significant explanatory power regarding rent prices - even after controlling for structural and locational apartment characteristics, and (2) harnessing floor plans results in an up to 10.56% lower out-of-sample prediction error. We further find that floor plans yield a particularly high gain in prediction performance for older and smaller apartments. Altogether, our empirical findings contribute to the existing research body by establishing the link between the visual design of floor plans and real estate prices. Moreover, our approach has important implications for online real estate platforms, which can use our findings to enhance user experience in their real estate listings.
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
Authors: Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
Date: 2021-02-16 00:00:00
Tasks: Multi-agent Reinforcement Learning, quantile regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II

Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE). However, such expected, i.e., risk-neutral, Q value is not sufficient even with CTDE due to the randomness of rewards and the uncertainty in environments, which causes the failure of these methods to train coordinating agents in complex environments. To address these issues, we propose RMIX, a novel cooperative MARL method with the Conditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. Specifically, we first learn the return distributions of individuals to analytically calculate CVaR for decentralized execution. Then, to handle the temporal nature of the stochastic outcomes during executions, we propose a dynamic risk level predictor for risk level tuning. Finally, we optimize the CVaR policies with CVaR values used to estimate the target in TD error during centralized training and the CVaR values are used as auxiliary local rewards to update the local distribution via Quantile Regression loss. Empirically, we show that our method significantly outperforms state-of-the-art methods on challenging StarCraft II tasks, demonstrating enhanced coordination and improved sample efficiency.