Publications

A Unifying Framework for Fairness-Aware Influence Maximization
Golnoosh Farnadi
Behrouz Babaki
Michel Gendreau
The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studi… (voir plus)ed over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms.
2020-04-19
Companion Proceedings of the Web Conference 2020 (publié)
doi.org
Role-Wise Data Augmentation for Knowledge Distillation
Jie Fu
Xue Geng
Zhijian Duan
Bohan Zhuang
Xingdi Yuan
Adam Trischler
Jie Lin
Vijay Chandrasekhar
Christopher Pal
Hao Dong
2020-04-18
ArXiv (prépublication)
openreview.net
openreview.net
Neuropsychiatric copy number variants exert shared effects on human brain structure
Claudia Modenato
Kuldeep Kumar
Clara Moreau
Sandra Martin‐Brevet
Guillaume Huguet
Catherine Schramm
Martineau Jean‐Louis
Charles-Olivier Martin
Nadine Younis
Petra Tamer
Élise Douard
Fanny Thébault‐Dagher
Valérie Côté
Audrey Charlebois
Florence Deguire
Anne Maillard
Borja Rodríguez‐Herreros
Aurélie Pain
Sonia Richetin
Leila Kushan … (voir 12 de plus)
Lester Melie‐García
Ana Isabel Silva
Marianne van den Bree
David E.J. Linden
Michael J. Owen
Jeremy Hall
Sarah Lippé
M. Mallar Chakravarty
Danilo Bzdok
Carrie E. Bearden
Bogdan Draganski
Sébastien Jacquemont
Neuropsychiatric CNVs share neuroanatomical signatures characterized by a parsimonious set of brain dimensions. The latter may underlie the … (voir plus)risk conferred by CNVs for a similar spectrum of neuropsychiatric conditions.
2020-04-16
medRxiv (prépublication)
doi.org
Uncertainty Evaluation Metric for Brain Tumour Segmentation
Raghav Mehta
Angelos Filos
Yarin Gal
Tal Arbel
In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in … (voir plus)the BraTS 2019 sub-challenge on uncertainty quantification. The metric is designed to: (1) reward uncertainty measures where high confidence is assigned to correct assertions, and where incorrect assertions are assigned low confidence and (2) penalize measures that have higher percentages of under-confident correct assertions. Here, the workings of the components of the metric are explored based on a number of popular uncertainty measures evaluated on the BraTS 2019 dataset.
2020-04-16
MIDL.io/2020/Conference (accepté)
openreview.net
openreview.net
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Miles Brundage
Shahar Avin
Jasmine Wang
Haydn Belfield
Gretchen Krueger
Gillian Hadfield
Heidy Khlaaf
Jingying Yang
Helen Toner
Ruth Fong
Tegan Maharaj
Pang Wei Koh
Sara Hooker
Jade Leung
Andrew Trask
Emma Bluemke
Jonathan Lebensold
Cullen O’Keefe
Mark Koren
Théo Ryffel … (voir 39 de plus)
JB Rubinovitz
Tamay Besiroglu
Federica Carugati
Jack Clark
Peter Eckersley
Sarah de Haas
Maritza Johnson
Ben Laurie
Alex Ingerman
Igor Krawczuk
Amanda Askell
Rosario Cammarota
Andrew Lohn
David Krueger
Charlotte Stix
Peter Henderson
Logan Graham
Carina Prunkl
Bianca Martin
Elizabeth Seger
Noa Zilberman
Seán Ó hÉigeartaigh
Frens Kroeger
Girish Sastry
Rebecca Kagan
Adrian Weller
Brian Tse
Elizabeth Barnes
Allan Dafoe
Paul Scharre
Ariel Herbert-Voss
Martijn Rasser
Shagun Sodhani
Carrick Flynn
Thomas Krendl Gilbert
Lisa Dyer
Saif Khan
Yoshua Bengio
Markus Anderljung
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and … (voir plus)recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
2020-04-14
arXiv (prépublication)
doi.org
arxiv.org
Clustering for Continuous-Time Hidden Markov Models.
Yu Luo
David A. Stephens
David L Buckeridge
We develop clustering procedures for longitudinal trajectories based on a continuous-time hidden Markov model (CTHMM) and a generalized line… (voir plus)ar observation model. Specifically in this paper, we carry out infinite mixture model-based clustering for CTHMM and achieve inference using Markov chain Monte Carlo (MCMC). Specifically, for Bayesian nonparametric inference using a Dirichlet process mixture model, we utilize restricted Gibbs sampling split-merge proposals to expedite the MCMC algorithm. We employ the proposed algorithm to the simulated data as well as a large real data example, and the results demonstrate the desired performance of the new sampler.
2020-04-12
(publié)
www.semanticscholar.org
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction
Vishal Jain
William Fedus
Hugo Larochelle
Doina Precup
Bellemare Marc-Emmanuel
Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatoriall… (voir plus)y large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.
2020-04-02
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal Mri Using Subtraction Images
Nazanin Mohammadi Sepahvand
Douglas Arnold
Tal Arbel
Accurate detection and segmentation of new lesional activity in longitudinal Magnetic Resonance Images (MRIs) of patients with Multiple Scle… (voir plus)rosis (MS) is important for monitoring disease activity, as well as for assessing treatment effects. In this work, we present the first deep learning framework to automatically detect and segment new and enlarging (NE) T2w lesions from longitudinal brain MRIs acquired from relapsing-remitting MS (RRMS) patients. The proposed framework is an adapted 3D U-Net [1] which includes as inputs the reference multi-modal MRI and T2-weighted lesion maps, as well an attention mechanism based on the subtraction MRI (between the two timepoints) which serves to assist the network in learning to differentiate between real anatomical change and artifactual change, while constraining the search space for small lesions. Experiments on a large, proprietary, multi -center, multi-modal, clinical trial dataset consisting of 1677 multi-modal scans illustrate that network achieves high overall detection accuracy (detection AUC=.95), outperforming (1) a U-Net without an attention mechanism (de-tection AUC=.93), (2) a framework based on subtracting independent T2-weighted segmentations (detection AUC=.57), and (3) DeepMedic (detection AUC=.84) [2], particularly for small lesions. In addition, the method was able to accurately classify patients as active/inactive with (sensitivities of. 69 and specificities of. 97).
2020-04-02
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (publié)
doi.org
Detecting semantic anomalies
Faruk Ahmed
Aaron Courville
We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmark… (voir plus)s. While the currently prevalent trend is to consider different datasets as OOD, we argue that out-distributions of practical interest are ones where the distinction is semantic in nature for a specified context, and that evaluative tasks should reflect this more closely. Assuming a context of object recognition, we recommend a set of benchmarks, motivated by practical applications. We make progress on these benchmarks by exploring a multi-task learning based approach, showing that auxiliary objectives for improved semantic awareness result in improved semantic anomaly detection, with accompanying generalization benefits.
2020-04-02
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Gifting in Multi-Agent Reinforcement Learning (Student Abstract)
Andrei Lupu
Doina Precup
This work performs a first study on multi-agent reinforcement learning with deliberate reward passing between agents. We empirically demonst… (voir plus)rate that such mechanics can greatly improve the learning progression in a resource appropriation setting and provide a preliminary discussion of the complex effects of gifting on the learning dynamics.
2020-04-02
AAAI Conference on Artificial Intelligence (publié)
doi.org
Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract)
Qizhen Zhang
Audrey Durand
Joelle Pineau
2020-04-02
AAAI Conference on Artificial Intelligence (publié)
doi.org
Modeling Dialogues with Hashcode Representations: A Nonparametric Approach
Sahil Garg
Irina Rish
Guillermo Cecchi
Palash Goyal
Shuyang Gao
Sarik Ghazarian
Greg Ver Steeg
Aram Galstyan
We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which le… (voir plus)arns hashcodes as text representations; unlike traditional deep learning models, it handles well relatively small datasets, while also scaling to large ones. We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses. As demonstrated on three real-life datasets, including prominently psychotherapy sessions, the proposed approach significantly outperforms several state-of-art neural network based dialogue systems, both in terms of computational efficiency, reducing training time from days or weeks to hours, and the response quality, achieving an order of magnitude improvement over competitors in frequency of being chosen as the best model by human evaluators.
2020-04-02
AAAI Conference on Artificial Intelligence (publié)
doi.org