pwc-archive/papers-with-abstracts huggingface dataset:
Individual Explanations in Machine Learning Models: A Case Study on Poverty Estimation
Authors: Alfredo Carrillo, Luis F. Cantú, Luis Tejerina, Alejandro Noriega
Date: 2021-04-09 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering

Machine learning methods are being increasingly applied in sensitive societal contexts, where decisions impact human lives. Hence it has become necessary to build capabilities for providing easily-interpretable explanations of models' predictions. Recently in academic literature, a vast number of explanations methods have been proposed. Unfortunately, to our knowledge, little has been documented about the challenges machine learning practitioners most often face when applying them in real-world scenarios. For example, a typical procedure such as feature engineering can make some methodologies no longer applicable. The present case study has two main objectives. First, to expose these challenges and how they affect the use of relevant and novel explanations methods. And second, to present a set of strategies that mitigate such challenges, as faced when implementing explanation methods in a relevant application domain -- poverty estimation and its use for prioritizing access to social policies.
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
Authors: Shweta Narkar, Yunfeng Zhang, Q. Vera Liao, Dakuo Wang, Justin D Weisz
Date: 2021-04-09 00:00:00
Tasks: AutoML, Explainable Artificial Intelligence (XAI), Feature Importance, model

Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models. A critical, final step of AutoML is human selection of a final model from dozens of candidates. In current AutoML systems, selection is supported only by performance metrics. Prior work has shown that in practice, people evaluate ML models based on additional criteria, such as the way a model makes predictions. Comparison may happen at multiple levels, from types of errors, to feature importance, to how the model makes predictions of specific instances. We developed \tool{} to support interactive model comparison for AutoML by integrating multiple Explainable AI (XAI) and visualization techniques. We conducted a user study in which we both evaluated the system and used it as a technology probe to understand how users perform model comparison in an AutoML system. We discuss design implications for utilizing XAI techniques for model comparison and supporting the unique needs of data scientists in comparing AutoML models.
Implementing Fair Regression In The Real World
Authors: Boris Ruf, Marcin Detyniecki
Date: 2021-04-09 00:00:00
Tasks: Fairness, regression

Most fair regression algorithms mitigate bias towards sensitive sub populations and therefore improve fairness at group level. In this paper, we investigate the impact of such implementation of fair regression on the individual. More precisely, we assess the evolution of continuous predictions from an unconstrained to a fair algorithm by comparing results from baseline algorithms with fair regression algorithms for the same data points. Based on our findings, we propose a set of post-processing algorithms to improve the utility of the existing fair regression approaches.
Towards Fine-grained Visual Representations by Combining Contrastive Learning with Image Reconstruction and Attention-weighted Pooling
Authors: Jonas Dippel, Steffen Vogler, Johannes Höhne
Date: 2021-04-09 00:00:00
Tasks: Contrastive Learning, Decoder, Image Reconstruction, Self-Supervised Learning

This paper presents Contrastive Reconstruction, ConRec - a self-supervised learning algorithm that obtains image representations by jointly optimizing a contrastive and a self-reconstruction loss. We showcase that state-of-the-art contrastive learning methods (e.g. SimCLR) have shortcomings to capture fine-grained visual features in their representations. ConRec extends the SimCLR framework by adding (1) a self-reconstruction task and (2) an attention mechanism within the contrastive learning task. This is accomplished by applying a simple encoder-decoder architecture with two heads. We show that both extensions contribute towards an improved vector representation for images with fine-grained visual features. Combining those concepts, ConRec outperforms SimCLR and SimCLR with Attention-Pooling on fine-grained classification datasets.
Batch Monte Carlo Tree Search
Authors: Tristan Cazenave
Date: 2021-04-09 00:00:00
Tasks: Game of Go, GPU

Making inferences with a deep neural network on a batch of states is much faster with a GPU than making inferences on one state after another. We build on this property to propose Monte Carlo Tree Search algorithms using batched inferences. Instead of using either a search tree or a transposition table we propose to use both in the same algorithm. The transposition table contains the results of the inferences while the search tree contains the statistics of Monte Carlo Tree Search. We also propose to analyze multiple heuristics that improve the search: the $\mu$ FPU, the Virtual Mean, the Last Iteration and the Second Move heuristics. They are evaluated for the game of Go using a MobileNet neural network.
Morpho-evolution with learning using a controller archive as an inheritance mechanism
Authors: Léni K. Le Goff, Edgar Buchanan, Emma Hart, Agoston E. Eiben, Wei Li, Matteo De Carlo, Alan F. Winfield, Matthew F. Hale, Robert Woolley, Mike Angus, Jon Timmis, Andy M. Tyrrell
Date: 2021-04-09 00:00:00

The joint optimisation of body-plan and control via evolutionary processes can be challenging in rich morphological spaces in which offspring can have body-plans that are very different from either of their parents. This causes a potential mismatch between the structure of an inherited controller and the new body. To address this, we propose a framework that combines an evolutionary algorithm to generate body-plans and a learning algorithm to optimise the parameters of a neural controller. The topology of this controller is created once the body-plan of each offspring body-plan is generated. The key novelty of the approach is to add an external archive for storing learned controllers that map to explicit `types' of robots (where this is defined with respect the features of the body-plan). By learning from a controller with an appropriate structure inherited from the archive, rather than from a randomly initialised one, we show that both the speed and magnitude of learning increases over time when compared to an approach that starts from scratch, using two tasks and three environments. The framework also provides new insights into the complex interactions between evolution and learning.
Counter-Strike Deathmatch with Large-Scale Behavioural Cloning
Authors: Tim Pearce, Jun Zhu
Date: 2021-04-09 00:00:00
Tasks: AI Agent, Behavioural cloning, FPS Games, Imitation Learning

This paper describes an AI agent that plays the popular first-person-shooter (FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input. The agent, a deep neural network, matches the performance of the medium difficulty built-in AI on the deathmatch game mode, whilst adopting a humanlike play style. Unlike much prior work in games, no API is available for CSGO, so algorithms must train and run in real-time. This limits the quantity of on-policy data that can be generated, precluding many reinforcement learning algorithms. Our solution uses behavioural cloning - training on a large noisy dataset scraped from human play on online servers (4 million frames, comparable in size to ImageNet), and a smaller dataset of high-quality expert demonstrations. This scale is an order of magnitude larger than prior work on imitation learning in FPS games.
Incorporating External Knowledge to Enhance Tabular Reasoning
Authors: J. Neeraja, Vivek Gupta, Vivek Srikumar
Date: 2021-04-09 00:00:00
Proceeding: NAACL 2021 4
Tasks: Natural Language Inference

Reasoning about tabular information presents unique challenges to modern NLP approaches which largely rely on pre-trained contextualized embeddings of text. In this paper, we study these challenges through the problem of tabular natural language inference. We propose easy and effective modifications to how information is presented to a model for this task. We show via systematic experiments that these strategies substantially improve tabular inference performance.
BERT-based Chinese Text Classification for Emergency Domain with a Novel Loss Function
Authors: Zhongju Wang, Long Wang, Chao Huang, Xiong Luo
Date: 2021-04-09 00:00:00
Tasks: Benchmarking, General Classification, Management, Text Categorization, text-classification, Text Classification

This paper proposes an automatic Chinese text categorization method for solving the emergency event report classification problem. Since bidirectional encoder representations from transformers (BERT) has achieved great success in natural language processing domain, it is employed to derive emergency text features in this study. To overcome the data imbalance problem in the distribution of emergency event categories, a novel loss function is proposed to improve the performance of the BERT-based model. Meanwhile, to avoid the impact of the extreme learning rate, the Adabound optimization algorithm that achieves a gradual smooth transition from Adam to SGD is employed to learn parameters of the model. To verify the feasibility and effectiveness of the proposed method, a Chinese emergency text dataset collected from the Internet is employed. Compared with benchmarking methods, the proposed method has achieved the best performance in terms of accuracy, weighted-precision, weighted-recall, and weighted-F1 values. Therefore, it is promising to employ the proposed method for real applications in smart emergency management systems.
INODE: Building an End-to-End Data Exploration System in Practice [Extended Vision]
Authors: Sihem Amer-Yahia, Georgia Koutrika, Frederic Bastian, Theofilos Belmpas, Martin Braschler, Ursin Brunner, Diego Calvanese, Maximilian Fabricius, Orest Gkini, Catherine Kosten, Davide Lanti, Antonis Litke, Hendrik Lücke-Tieke, Francesco Alessandro Massucci, Tarcisio Mendes de Farias, Alessandro Mosca, Francesco Multari, Nikolaos Papadakis, Dimitris Papadopoulos, Yogendra Patil, Aurélien Personnaz, Guillem Rull, Ana Sima, Ellery Smith, Dimitrios Skoutas, Srividya Subramanian, Guohui Xiao, Kurt Stockinger
Date: 2021-04-09 00:00:00
Tasks: Management

A full-fledged data exploration system must combine different access modalities with a powerful concept of guiding the user in the exploration process, by being reactive and anticipative both for data discovery and for data linking. Such systems are a real opportunity for our community to cater to users with different domain and data science expertise. We introduce INODE -- an end-to-end data exploration system -- that leverages, on the one hand, Machine Learning and, on the other hand, semantics for the purpose of Data Management (DM). Our vision is to develop a classic unified, comprehensive platform that provides extensive access to open datasets, and we demonstrate it in three significant use cases in the fields of Cancer Biomarker Reearch, Research and Innovation Policy Making, and Astrophysics. INODE offers sustainable services in (a) data modeling and linking, (b) integrated query processing using natural language, (c) guidance, and (d) data exploration through visualization, thus facilitating the user in discovering new insights. We demonstrate that our system is uniquely accessible to a wide range of users from larger scientific communities to the public. Finally, we briefly illustrate how this work paves the way for new research opportunities in DM.
SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size
Authors: Jessica Yung, Rob Romijnders, Alexander Kolesnikov, Lucas Beyer, Josip Djolonga, Neil Houlsby, Sylvain Gelly, Mario Lucic, Xiaohua Zhai
Date: 2021-04-09 00:00:00
Tasks: BIG-bench Machine Learning

Before deploying machine learning models it is critical to assess their robustness. In the context of deep neural networks for image understanding, changing the object location, rotation and size may affect the predictions in non-trivial ways. In this work we perform a fine-grained analysis of robustness with respect to these factors of variation using SI-Score, a synthetic dataset. In particular, we investigate ResNets, Vision Transformers and CLIP, and identify interesting qualitative differences between these.
eGAN: Unsupervised approach to class imbalance using transfer learning
Authors: Ademola Okerinde, Lior Shamir, William Hsu, Tom Theis, Nasik Nafi
Date: 2021-04-09 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Generative Adversarial Network, image-classification, Image Classification, Specificity, Transfer Learning

Class imbalance is an inherent problem in many machine learning classification tasks. This often leads to trained models that are unusable for any practical purpose. In this study we explore an unsupervised approach to address these imbalances by leveraging transfer learning from pre-trained image classification models to encoder-based Generative Adversarial Network (eGAN). To the best of our knowledge, this is the first work to tackle this problem using GAN without needing to augment with synthesized fake images. In the proposed approach we use the discriminator network to output a negative or positive score. We classify as minority, test samples with negative scores and as majority those with positive scores. Our approach eliminates epistemic uncertainty in model predictions, as the P(minority) + P(majority) need not sum up to 1. The impact of transfer learning and combinations of different pre-trained image classification models at the generator and discriminator is also explored. Best result of 0.69 F1-score was obtained on CIFAR-10 classification task with imbalance ratio of 1:2500. Our approach also provides a mechanism of thresholding the specificity or sensitivity of our machine learning system. Keywords: Class imbalance, Transfer Learning, GAN, nash equilibrium
Sequential Online Chore Division for Autonomous Vehicle Convoy Formation
Authors: Harel Yedidsion, Shani Alkoby, Peter Stone
Date: 2021-04-09 00:00:00

Chore division is a class of fair division problems in which some undesirable "resource" must be shared among a set of participants, with each participant wanting to get as little as possible. Typically the set of participants is fixed and known at the outset. This paper introduces a novel variant, called sequential online chore division (SOCD), in which participants arrive and depart online, while the chore is being performed: both the total number of participants and their arrival/departure times are initially unknown. In SOCD, exactly one agent must be performing the chore at any give time (e.g. keeping lookout), and switching the performer incurs a cost. In this paper, we propose and analyze three mechanisms for SOCD: one centralized mechanism using side payments, and two distributed ones that seek to balance the participants' loads. Analysis and results are presented in a domain motivated by autonomous vehicle convoy formation, where the chore is leading the convoy so that all followers can enjoy reduced wind resistance.
Individual Explanations in Machine Learning Models: A Survey for Practitioners
Authors: Alfredo Carrillo, Luis F. Cantú, Alejandro Noriega
Date: 2021-04-09 00:00:00
Tasks: BIG-bench Machine Learning, Survey

In recent years, the use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise. Although these models can often bring substantial improvements in the accuracy and efficiency of organizations, many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways. Hence, these models are often regarded as black-boxes, in the sense that their internal mechanisms can be opaque to human audit. In real-world applications, particularly in domains where decisions can have a sensitive impact--e.g., criminal justice, estimating credit scores, insurance risk, health risks, etc.--model interpretability is desired. Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models. This survey reviews the most relevant and novel methods that form the state-of-the-art for addressing the particular problem of explaining individual instances in machine learning. It seeks to provide a succinct review that can guide data science and machine learning practitioners in the search for appropriate methods to their problem domain.
Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS
Authors: Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonanthan Beich, Jyotishman Pathak, Amit Sheth
Date: 2021-04-09 00:00:00
Tasks: Diagnostic

Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.
Towards Agrobots: Trajectory Control of an Autonomous Tractor Using Type-2 Fuzzy Logic Controllers
Authors: Erdal Kayacan, Erkan Kayacan, Herman Ramon, Okyay Kaynak, Wouter Saeys
Date: 2021-04-09 00:00:00
Tasks: Autonomous Navigation

Provision of some autonomous functions to an agricultural vehicle would lighten the job of the operator but in doing so, the accuracy should not be lost to still obtain an optimal yield. Autonomous navigation of an agricultural vehicle involves the control of different dynamic subsystems, such as the yaw angle dynamics and the longitudinal speed dynamics. In this study, a proportional-integral-derivative controller is used to control the longitudinal velocity of the tractor. For the control of the yaw angle dynamics, a proportional-derivative controller works in parallel with a type-2 fuzzy neural network. In such an arrangement, the former ensures the stability of the related subsystem, while the latter learns the system dynamics and becomes the leading controller. In this way, instead of modeling the interactions between the subsystems prior to the design of model-based control, we develop a control algorithm which learns the interactions online from the measured feedback error. In addition to the control of the stated subsystems, a kinematic controller is needed to correct the errors in both the x- and the y- axis for the trajectory tracking problem of the tractor. To demonstrate the real-time abilities of the proposed control scheme, an autonomous tractor is equipped with the use of reasonably priced sensors and actuators. Experimental results show the efficacy and efficiency of the proposed learning algorithm.
Fast, Smart Neuromorphic Sensors Based on Heterogeneous Networks and Mixed Encodings
Authors: Angel Yanguas-Gil
Date: 2021-04-09 00:00:00

Neuromorphic architectures are ideally suited for the implementation of smart sensors able to react, learn, and respond to a changing environment. Our work uses the insect brain as a model to understand how heterogeneous architectures, incorporating different types of neurons and encodings, can be leveraged to create systems integrating input processing, evaluation, and response. Here we show how the combination of time and rate encodings can lead to fast sensors that are able to generate a hypothesis on the input in only a few cycles and then use that hypothesis as secondary input for more detailed analysis.
XFORMAL: A Benchmark for Multilingual Formality Style Transfer
Authors: Eleftheria Briakou, Di Lu, Ke Zhang, Joel Tetreault
Date: 2021-04-08 00:00:00
Tasks: Formality Style Transfer, Style Transfer

We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian. Results on XFORMAL suggest that state-of-the-art style transfer approaches perform close to simple baselines, indicating that style transfer is even more challenging when moving multilingual.
Risk-Aware Lane Selection on Highway with Dynamic Obstacles
Authors: Sangjae Bae, David Isele, Kikuo Fujimura, Scott J. Moura
Date: 2021-04-08 00:00:00
Tasks: Model Predictive Control

This paper proposes a discretionary lane selection algorithm. In particular, highway driving is considered as a targeted scenario, where each lane has a different level of traffic flow. When lane-changing is discretionary, it is advised not to change lanes unless highly beneficial, e.g., reducing travel time significantly or securing higher safety. Evaluating such "benefit" is a challenge, along with multiple surrounding vehicles in dynamic speed and heading with uncertainty. We propose a real-time lane-selection algorithm with careful cost considerations and with modularity in design. The algorithm is a search-based optimization method that evaluates uncertain dynamic positions of other vehicles under a continuous time and space domain. For demonstration, we incorporate a state-of-the-art motion planner framework (Neural Networks integrated Model Predictive Control) under a CARLA simulation environment.
Generative Landmarks
Authors: David Ferman, Gaurav Bharaj
Date: 2021-04-08 00:00:00
Tasks: Generative Adversarial Network, Translation

We propose a general purpose approach to detect landmarks with improved temporal consistency, and personalization. Most sparse landmark detection methods rely on laborious, manually labelled landmarks, where inconsistency in annotations over a temporal volume leads to sub-optimal landmark learning. Further, high-quality landmarks with personalization is often hard to achieve. We pose landmark detection as an image translation problem. We capture two sets of unpaired marked (with paint) and unmarked videos. We then use a generative adversarial network and cyclic consistency to predict deformations of landmark templates that simulate markers on unmarked images until these images are indistinguishable from ground-truth marked images. Our novel method does not rely on manually labelled priors, is temporally consistent, and image class agnostic -- face, and hand landmarks detection examples are shown.