pwc-archive/papers-with-abstracts huggingface dataset:
Intrinsic Exploration as Multi-Objective RL
Authors: Philippe Morere, Fabio Ramos
Date: 2020-04-06 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

Intrinsic motivation enables reinforcement learning (RL) agents to explore when rewards are very sparse, where traditional exploration heuristics such as Boltzmann or e-greedy would typically fail. However, intrinsic exploration is generally handled in an ad-hoc manner, where exploration is not treated as a core objective of the learning process; this weak formulation leads to sub-optimal exploration performance. To overcome this problem, we propose a framework based on multi-objective RL where both exploration and exploitation are being optimized as separate objectives. This formulation brings the balance between exploration and exploitation at a policy level, resulting in advantages over traditional methods. This also allows for controlling exploration while learning, at no extra cost. Such strategies achieve a degree of control over agent exploration that was previously unattainable with classic or intrinsic rewards. We demonstrate scalability to continuous state-action spaces by presenting a method (EMU-Q) based on our framework, guiding exploration towards regions of higher value-function uncertainty. EMU-Q is experimentally shown to outperform classic exploration techniques and other intrinsic RL methods on a continuous control benchmark and on a robotic manipulator.
AutoToon: Automatic Geometric Warping for Face Cartoon Generation
Authors: Julia Gong, Yannick Hold-Geoffroy, Jingwan Lu
Date: 2020-04-06 00:00:00
Tasks: Caricature

Caricature, a type of exaggerated artistic portrait, amplifies the distinctive, yet nuanced traits of human faces. This task is typically left to artists, as it has proven difficult to capture subjects' unique characteristics well using automated methods. Recent development of deep end-to-end methods has achieved promising results in capturing style and higher-level exaggerations. However, a key part of caricatures, face warping, has remained challenging for these systems. In this work, we propose AutoToon, the first supervised deep learning method that yields high-quality warps for the warping component of caricatures. Completely disentangled from style, it can be paired with any stylization method to create diverse caricatures. In contrast to prior art, we leverage an SENet and spatial transformer module and train directly on artist warping fields, applying losses both prior to and after warping. As shown by our user studies, we achieve appealing exaggerations that amplify distinguishing features of the face while preserving facial detail.
Exploring Early Prediction of Buyer-Seller Negotiation Outcomes
Authors: Kushal Chawla, Gale Lucas, Jonathan May, Jonathan Gratch
Date: 2020-04-06 00:00:00
Tasks: Language Modeling, Language Modelling, Prediction, Sentence

Agents that negotiate with humans find broad applications in pedagogy and conversational AI. Most efforts in human-agent negotiations rely on restrictive menu-driven interfaces for communication. To advance the research in language-based negotiation systems, we explore a novel task of early prediction of buyer-seller negotiation outcomes, by varying the fraction of utterances that the model can access. We explore the feasibility of early prediction by using traditional feature-based methods, as well as by incorporating the non-linguistic task context into a pretrained language model using sentence templates. We further quantify the extent to which linguistic features help in making better predictions apart from the task-specific price information. Finally, probing the pretrained model helps us to identify specific features, such as trust and agreement, that contribute to the prediction performance.
Moving Metric Detection and Alerting System at eBay
Authors: Zezhong Zhang, Keyu Nie, Ted Tao Yuan
Date: 2020-04-06 00:00:00
Tasks: Anomaly Detection, Retrieval, valid

At eBay, there are thousands of product health metrics for different domain teams to monitor. We built a two-phase alerting system to notify users with actionable alerts based on anomaly detection and alert retrieval. In the first phase, we developed an efficient anomaly detection algorithm, called Moving Metric Detector (MMD), to identify potential alerts among metrics with distribution agnostic criteria. In the second alert retrieval phase, we built additional logic with feedbacks to select valid actionable alerts with point-wise ranking model and business rules. Compared with other trend and seasonality decomposition methods, our decomposer is faster and better to detect anomalies in unsupervised cases. Our two-phase approach dramatically improves alert precision and avoids alert spamming in eBay production.
Deep Neural Network in Cusp Catastrophe Model
Authors: Ranadeep Daw, Zhuoqiong He
Date: 2020-04-06 00:00:00
Tasks: model

Catastrophe theory was originally proposed to study dynamical systems that exhibit sudden shifts in behavior arising from small changes in input. These models can generate reasonable explanation behind abrupt jumps in nonlinear dynamic models. Among the different catastrophe models, the Cusp Catastrophe model attracted the most attention due to it's relatively simpler dynamics and rich domain of application. Due to the complex behavior of the response, the parameter space becomes highly non-convex and hence it becomes very hard to optimize to figure out the generating parameters. Instead of solving for these generating parameters, we demonstrated how a Machine learning model can be trained to learn the dynamics of the Cusp catastrophe models, without ever really solving for the generating model parameters. Simulation studies and application on a few famous datasets are used to validate our approach. To our knowledge, this is the first paper of such kind where a neural network based approach has been applied in Cusp Catastrophe model.
Adaptive Explainable Neural Networks (AxNNs)
Authors: Jie Chen, Joel Vaughan, Vijayan N. Nair, Agus Sudjianto
Date: 2020-04-05 00:00:00
Tasks: Distributed Computing

While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the results. We develop a new framework called Adaptive Explainable Neural Networks (AxNN) for achieving the dual goals of good predictive performance and model interpretability. For predictive performance, we build a structured neural network made up of ensembles of generalized additive model networks and additive index models (through explainable neural networks) using a two-stage process. This can be done using either a boosting or a stacking ensemble. For interpretability, we show how to decompose the results of AxNN into main effects and higher-order interaction effects. The computations are inherited from Google's open source tool AdaNet and can be efficiently accelerated by training with distributed computing. The results are illustrated on simulated and real datasets.
TAPAS: Weakly Supervised Table Parsing via Pre-training
Authors: Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno, Julian Martin Eisenschlos
Date: 2020-04-05 00:00:00
Proceeding: ACL 2020 6
Tasks: Question Answering, Semantic Parsing, Transfer Learning

Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition, the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation operator to such selection. TAPAS extends BERT's architecture to encode tables as input, initializes from an effective joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with three different semantic parsing datasets, and find that TAPAS outperforms or rivals semantic parsing models by improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WIKISQL and WIKITQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.
Domain-based Latent Personal Analysis and its use for impersonation detection in social media
Authors: Osnat Mokryn, Hagit Ben-Shoshan
Date: 2020-04-05 00:00:00
Tasks: Authorship Attribution

Zipf's law defines an inverse proportion between a word's ranking in a given corpus and its frequency in it, roughly dividing the vocabulary into frequent words and infrequent ones. Here, we stipulate that within a domain an author's signature can be derived from, in loose terms, the author's missing popular words and frequently used infrequent-words. We devise a method, termed Latent Personal Analysis (LPA), for finding domain-based attributes for entities in a domain: their distance from the domain and their signature, which determines how they most differ from a domain. We identify the most suitable distance metric for the method among several and construct the distances and personal signatures for authors, the domain's entities. The signature consists of both over-used terms (compared to the average), and missing popular terms. We validate the correctness and power of the signatures in identifying users and set existence conditions. We then show uses for the method in explainable authorship attribution: we define algorithms that utilize LPA to identify two types of impersonation in social media: (1) authors with sockpuppets (multiple) accounts; (2) front users accounts, operated by several authors. We validate the algorithms and employ them over a large scale dataset obtained from a social media site with over 4000 users. We corroborate these results using temporal rate analysis. LPA can further be used to devise personal attributes in a wide range of scientific domains in which the constituents have a long-tail distribution of elements.
Enhancing Social Recommendation with Adversarial Graph Convolutional Networks
Authors: Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, Lizhen Cui
Date: 2020-04-05 00:00:00
Tasks: Recommendation Systems

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. However, recent reports from industry show that social recommender systems consistently fail in practice. According to the negative findings, the failure is attributed to: (1) A majority of users only have a very limited number of neighbors in social networks and can hardly benefit from social relations; (2) Social relations are noisy but they are indiscriminately used; (3) Social relations are assumed to be universally applicable to multiple scenarios while they are actually multi-faceted and show heterogeneous strengths in different scenarios. Most existing social recommendation models only consider the homophily in social networks and neglect these drawbacks. In this paper we propose a deep adversarial framework based on graph convolutional networks (GCN) to address these problems. Concretely, for (1) and (2), a GCN-based autoencoder is developed to augment the relation data by encoding high-order and complex connectivity patterns, and meanwhile is optimized subject to the constraint of reconstructing the social profile to guarantee the validity of the identified neighborhood. After obtaining enough purified social relations for each user, a GCN-based attentive social recommendation module is designed to address (3) by capturing the heterogeneous strengths of social relations. Finally, we adopt adversarial training to unify all the components by playing a Minimax game and ensure a coordinated effort to enhance recommendation performance. Extensive experiments on multiple open datasets demonstrate the superiority of our framework and the ablation study confirms the importance and effectiveness of each component.
Distributed Estimation for Principal Component Analysis: an Enlarged Eigenspace Analysis
Authors: Xi Chen, Jason D. Lee, He Li, Yun Yang
Date: 2020-04-05 00:00:00

The growing size of modern data sets brings many challenges to the existing statistical estimation approaches, which calls for new distributed methodologies. This paper studies distributed estimation for a fundamental statistical machine learning problem, principal component analysis (PCA). Despite the massive literature on top eigenvector estimation, much less is presented for the top-$L$-dim ($L>1$) eigenspace estimation, especially in a distributed manner. We propose a novel multi-round algorithm for constructing top-$L$-dim eigenspace for distributed data. Our algorithm takes advantage of shift-and-invert preconditioning and convex optimization. Our estimator is communication-efficient and achieves a fast convergence rate. In contrast to the existing divide-and-conquer algorithm, our approach has no restriction on the number of machines. Theoretically, the traditional Davis-Kahan theorem requires the explicit eigengap assumption to estimate the top-$L$-dim eigenspace. To abandon this eigengap assumption, we consider a new route in our analysis: instead of exactly identifying the top-$L$-dim eigenspace, we show that our estimator is able to cover the targeted top-$L$-dim population eigenspace. Our distributed algorithm can be applied to a wide range of statistical problems based on PCA, such as principal component regression and single index model. Finally, We provide simulation studies to demonstrate the performance of the proposed distributed estimator.
Finding the Optimal Vocabulary Size for Neural Machine Translation
Authors: Thamme Gowda, Jonathan May
Date: 2020-04-05 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Classification, General Classification, Machine Translation, NMT, Translation

We cast neural machine translation (NMT) as a classification task in an autoregressive setting and analyze the limitations of both classification and autoregression components. Classifiers are known to perform better with balanced class distributions during training. Since the Zipfian nature of languages causes imbalanced classes, we explore its effect on NMT. We analyze the effect of various vocabulary sizes on NMT performance on multiple languages with many data sizes, and reveal an explanation for why certain vocabulary sizes are better than others.
Stage I non-small cell lung cancer stratification by using a model-based clustering algorithm with covariates
Authors: Carlos Relvas, André Fujita
Date: 2020-04-05 00:00:00
Tasks: Clustering, Prognosis

Lung cancer is currently the leading cause of cancer deaths. Among various subtypes, the number of patients diagnosed with stage I non-small cell lung cancer (NSCLC), particularly adenocarcinoma, has been increasing. It is estimated that 30 - 40\% of stage I patients will relapse, and 10 - 30\% will die due to recurrence, clearly suggesting the presence of a subgroup that could be benefited by additional therapy. We hypothesize that current attempts to identify stage I NSCLC subgroup failed due to covariate effects, such as the age at diagnosis and differentiation, which may be masking the results. In this context, to stratify stage I NSCLC, we propose CEM-Co, a model-based clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process. We applied CEM-Co on a gene expression data set composed of 129 subjects diagnosed with stage I NSCLC and successfully identified a subgroup with a significantly different phenotype (poor prognosis), while standard clustering algorithms failed.
Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics
Authors: Simon Jenni, Hailin Jin, Paolo Favaro
Date: 2020-04-05 00:00:00
Proceeding: CVPR 2020 6

We introduce a novel principle for self-supervised feature learning based on the discrimination of specific transformations of an image. We argue that the generalization capability of learned features depends on what image neighborhood size is sufficient to discriminate different image transformations: The larger the required neighborhood size and the more global the image statistics that the feature can describe. An accurate description of global image statistics allows to better represent the shape and configuration of objects and their context, which ultimately generalizes better to new tasks such as object classification and detection. This suggests a criterion to choose and design image transformations. Based on this criterion, we introduce a novel image transformation that we call limited context inpainting (LCI). This transformation inpaints an image patch conditioned only on a small rectangular pixel boundary (the limited context). Because of the limited boundary information, the inpainter can learn to match local pixel statistics, but is unlikely to match the global statistics of the image. We claim that the same principle can be used to justify the performance of transformations such as image rotations and warping. Indeed, we demonstrate experimentally that learning to discriminate transformations such as LCI, image warping and rotations, yields features with state of the art generalization capabilities on several datasets such as Pascal VOC, STL-10, CelebA, and ImageNet. Remarkably, our trained features achieve a performance on Places on par with features trained through supervised learning with ImageNet labels.
Online information on medical cannabis may rise unrealistic expectations and downplay potential side effects
Authors: Arthur Cassa Macedo, André Oliveira Vilela de Faria, Isabella Bizzi, Fabrício A. Moreira, Alessandro Colasanti, Pietro Ghezzi
Date: 2020-04-05 00:00:00

There is a growing literature on the potential medical uses of Cannabis sativa and cannabinoid compounds. Although these have only been approved by regulatory agencies for few indications, there is a hype about their possible benefits in a variety of conditions and a large market in the wellness industry. As in many cases patients search for information on cannabis products online, we have analyzed the information on medical cannabis available on the Internet. Analyzing 176 webpages returned by a search engine, we found that more than half of them were news websites. Pain, epilepsy and multiple sclerosis were the most frequently therapeutic areas mentioned by the webpages, which did not always match those for which there is regulatory approval. Information was also incomplete, with only 22% of the webpages mentioning potential side effects. Health portal websites provided the most complete information. On average, 80% of webpages had a neutral stance on the potential benefits of medical cannabis, with commercial websites having more frequently a positive stance (67%). We conclude that the information that can be found online could raise unrealistic expectations regarding therapeutic areas for which science-based evidence is often still weak.
Asymptotic normality of robust risk minimizers
Authors: Stanislav Minsker
Date: 2020-04-05 00:00:00

This paper investigates asymptotic properties of algorithms that can be viewed as robust analogues of the classical empirical risk minimization. These strategies are based on replacing the usual empirical average by a robust proxy of the mean, such as the (version of) the median of means estimator. It is well known by now that the excess risk of resulting estimators often converges to zero at optimal rates under much weaker assumptions than those required by their ``classical'' counterparts. However, less is known about the asymptotic properties of the estimators themselves, for instance, whether robust analogues of the maximum likelihood estimators are asymptotically efficient. We make a step towards answering these questions and show that for a wide class of parametric problems, minimizers of the appropriately defined robust proxy of the risk converge to the minimizers of the true risk at the same rate, and often have the same asymptotic variance, as the estimators obtained by minimizing the usual empirical risk.
XtracTree: a Simple and Effective Method for Regulator Validation of Bagging Methods Used in Retail Banking
Authors: Jeremy Charlier, Vladimir Makarenkov
Date: 2020-04-05 00:00:00

Bootstrap aggregation, known as bagging, is one of the most popular ensemble methods used in machine learning (ML). An ensemble method is a ML method that combines multiple hypotheses to form a single hypothesis used for prediction. A bagging algorithm combines multiple classifiers modeled on different sub-samples of the same data set to build one large classifier. Banks, and their retail banking activities, are nowadays using the power of ML algorithms, including decision trees and random forests, to optimize their processes. However, banks have to comply with regulators and governance and, hence, delivering effective ML solutions is a challenging task. It starts with the bank's validation and governance department, followed by the deployment of the solution in a production environment up to the external validation of the national financial regulator. Each proposed ML model has to be validated and clear rules for every algorithm-based decision must be justified. In this context, we propose XtracTree, an algorithm capable of efficiently converting an ML bagging classifier, such as a random forest, into simple "if-then" rules satisfying the requirements of model validation. We use a public loan data set from Kaggle to illustrate the usefulness of our approach. Our experiments demonstrate that using XtracTree, one can convert an ML model into a rule-based algorithm, leading to easier model validation by national financial regulators and the bank's validation department. The proposed approach allowed our banking institution to reduce up to 50% the time of delivery of our AI solutions to the end-user.
Hyper-spectral NIR and MIR data and optimal wavebands for detection of apple tree diseases
Authors: Dmitrii Shadrin, Mariia Pukalchik, Anastasia Uryasheva, Evgeny Tsykunov, Grigoriy Yashin, Nikita Rodichenko, Dzmitry Tsetserukou
Date: 2020-04-05 00:00:00

Plant diseases can lead to dramatic losses in yield and quality of food, becoming a problem of high priority for farmers. Apple scab, moniliasis, and powdery mildew are the most significant apple tree diseases worldwide and may cause between 50% and 60% in yield losses annually; they are controlled by fungicide use with huge financial and time expenses. This research proposes a modern approach for analyzing the spectral data in Near-Infrared and Mid-Infrared ranges of the apple tree diseases at different stages. Using the obtained spectra, we found optimal spectral bands for detecting particular disease and discriminating it from other diseases and healthy trees. The proposed instrument will provide farmers with accurate, real-time information on different stages of apple tree diseases, enabling more effective timing, and selecting the fungicide application, resulting in better control and increasing yield. The obtained dataset, as well as scripts in Matlab for processing data and finding optimal spectral bands, are available via the link: https://yadi.sk/d/ZqfGaNlYVR3TUA