Irina Rish

sayed.mansouri-tehrani@mila.quebec

Amin Darabi

PhD - Université de Montréal

amin.darabi@mila.quebec

Amin Memarian

Independent visiting researcher

memariaa@mila.quebec

Amin Mansouri

Master's Research - Université de Montréal

andrew.williams@mila.quebec

Andrei Mircea Romascanu

PhD - Université de Montréal

PhD - Université de Montréal

arian.khorasani@mila.quebec

Arian Khorasani

Master's Research - Université de Montréal

arnav-kumar.jain@mila.quebec

Arjun Ashok

PhD

Co-supervisor :

Alexandre Drouin

arjun.ashok@mila.quebec

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Collaborating researcher

ayush.kaushal@mila.quebec

Benjamin Therien

PhD - Université de Montréal

Co-supervisor :

Eugene Belilovsky

benjamin.therien@mila.quebec

Collaborating researcher - Université de Montréal

connor.brennan@mila.quebec

Daria Yasafova

Research Intern - Technical University of Munich

daria.yasafova@mila.quebec

Dave Whipps

Master's Research - Université de Montréal

whippsda@mila.quebec

diganta.misra@mila.quebec

Diganta Misra

Master's Research - Université de Montréal

Postdoctorate

Principal supervisor :

Nicolas Le Roux

ekaterina.lobacheva@mila.quebec

PhD - McGill University

Principal supervisor :

Blake Richards

ethan.caballero@mila.quebec

george.adamopoulos@mila.quebec

George Adamopoulos

Research Intern

gopeshh.subbaraj@mila.quebec

Germán Abrevaya

Independent visiting researcher - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

gwendolyne.legate@mila.quebec

Gwen Legate

PhD - Concordia University

Principal supervisor :

Eugene Belilovsky

Ivan Anokhin

PhD - Université de Montréal

Co-supervisor :

Samira Ebrahimi Kahou

ivan.anokhin@mila.quebec

juan.mayor-torres@mila.quebec

Juan Manuel Mayor-Torres

Collaborating researcher

Collaborating Alumni - Université de Montréal

Co-supervisor :

Sarath Chandar Anbil Parthipan

kshitij.gupta@mila.quebec

Mahta Ramezanian

Master's Research - Université de Montréal

Co-supervisor :

Guillaume Dumas

mahta.ramezanian@mila.quebec

Matthew Riemer

PhD - Université de Montréal

matthew.riemer@mila.quebec

Maximilian Puelma Touzel

Collaborating researcher

PhD - Université de Montréal

arefinmr@mila.quebec

Mohammad Pezeshki

Collaborating researcher

pezeshki@mila.quebec

Mohammad-Javad Darvishi Bayazi

PhD - Université de Montréal

mohammad-javad.darvishi-bayasi@mila.quebec

PhD - Université de Montréal

faramarm@mila.quebec

Motahareh Pourrahimi

PhD - McGill University

Principal supervisor :

Pouya Bashivan

motahareh.pourrahimi@mila.quebec

nadhir.hassen@mila.quebec

Nadhir Hassen

Research Intern - Université de Montréal

Neeraj Kumar

Professional Master's - Université de Montréal

neeraj.kumar@mila.quebec

Nizar Islah

PhD - Université de Montréal

Principal supervisor :

nizar.islah@mila.quebec

paolo.cudrano@mila.quebec

Omar Younis

Research Intern - Université de Montréal

omar.younis@mila.quebec

Collaborating researcher - Politecnico di Milano

pascal.tikeng@mila.quebec

Pascal Tikeng Notsawo

PhD - Université de Montréal

Co-supervisor :

Guillaume Dumas

Collaborating researcher

prateek.humane@mila.quebec

Master's Research - Université de Montréal

remus.mocanu@mila.quebec

Reza Bayat

Master's Research - Université de Montréal

Co-supervisor :

Pouya Bashivan

reza.bayat@mila.quebec

rishika.bhagwatkar@mila.quebec

Rishika Bhagwatkar

Master's Research - Université de Montréal

Collaborating researcher - Université de Montréal

roland.riachi@mila.quebec

Simon Dufort-Labbé

PhD - Université de Montréal

simon.dufort-labbe@mila.quebec

Sparsha Mishra

Master's Research - Université de Montréal

sparsha.mishra@mila.quebec

Tejas Vaidhya

Master's Research - Université de Montréal

tejas.vaidhya@mila.quebec

PhD - Université de Montréal

Co-supervisor :

vaibhav.singh@mila.quebec

timothy.nest@mila.quebec

Vaibhav Singh

PhD - Concordia University

Principal supervisor :

Eugene Belilovsky

Zahra Sheikhbahaee

Postdoctorate - Université de Montréal

Principal supervisor :

Guillaume Dumas

zahra.sheikhbahaee@mila.quebec

Publications

Understanding Continual Learning Settings with Data Distribution Drift Analysis

Timothee LESORT

Massimo Caccia

Classical machine learning algorithms often assume that the data are drawn i.i.d. from a stationary probability distribution. Recently, cont… (see more)inual learning emerged as a rapidly growing area of machine learning where this assumption is relaxed, i.e. where the data distribution is non-stationary and changes over time. This paper represents the state of data distribution by a context variable

2021-04-04

ArXiv (preprint)

Predicting Infectiousness for Proactive Contact Tracing

Prateek Gupta

Tegan Maharaj

Nasim Rahaman

Martin Weiss

Tristan Deleu

Meng Qu

Victor Schmidt

Pierre-Luc St-Charles

Hannah Alsdurf

Olexa Bilaniuk

David Buckeridge

gaetan caron

pierre luc carrier

Joumana Ghosn

satya ortiz gagne

Chris Pal

Bernhard Schölkopf … (see 3 more)

abhinav sharma

Jian Tang

andrew williams

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (see more)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.

2021-01-12

ICLR.cc/2021/Conference (spotlight)

Adversarial Feature Desensitization

Pouya Bashivan

Reza Bayat

Adam Ibrahim

Kartik Ahuja

Mojtaba Faramarzi

Touraj Laleh

Blake Richards

Neural networks are known to be vulnerable to adversarial attacks -- slight but carefully constructed perturbations of the inputs which can … (see more)drastically impair the network's performance. Many defense methods have been proposed for improving robustness of deep networks by training them on adversarially perturbed inputs. However, these models often remain vulnerable to new types of attacks not seen during training, and even to slightly stronger versions of previously seen attacks. In this work, we propose a novel approach to adversarial robustness, which builds upon the insights from the domain adaptation field. Our method, called Adversarial Feature Desensitization (AFD), aims at learning features that are invariant towards adversarial perturbations of the inputs. This is achieved through a game where we learn features that are both predictive and robust (insensitive to adversarial attacks), i.e. cannot be used to discriminate between natural and adversarial data. Empirical results on several benchmarks demonstrate the effectiveness of the proposed approach against a wide range of attack types and attack strengths. Our code is available at https://github.com/BashivanLab/afd.

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization

Kartik Ahuja

Ethan Caballero

Dinghuai Zhang

Jean-Christophe Gagnon-Audet

Ioannis Mitliagkas

The invariance principle from causality is at the heart of notable approaches such as invariant risk minimization (IRM) that seek to address… (see more) out-of-distribution (OOD) generalization failures. Despite the promising theory, invariance principle-based approaches fail in common classification tasks, where invariant (causal) features capture all the information about the label. Are these failures due to the methods failing to capture the invariance? Or is the invariance principle itself insufficient? To answer these questions, we revisit the fundamental assumptions in linear regression tasks, where invariance-based approaches were shown to provably generalize OOD. In contrast to the linear regression tasks, we show that for linear classification tasks we need much stronger restrictions on the distribution shifts, or otherwise OOD generalization is impossible. Furthermore, even with appropriate restrictions on distribution shifts in place, we show that the invariance principle alone is insufficient. We prove that a form of the information bottleneck constraint along with invariance helps address key failures when invariant features capture all the information about the label and also retains the existing success when they do not. We propose an approach that incorporates both of these principles and demonstrate its effectiveness in several experiments.

COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing

Prateek Gupta

Tegan Maharaj

Martin Weiss

Nasim Rahaman

Hannah Alsdurf

abhinav sharma

Nanor Minoyan

Soren Harnois-Leblanc

Victor Schmidt

Pierre-Luc St-Charles

Tristan Deleu

andrew williams

Akshay Patel

Meng Qu

Olexa Bilaniuk

gaetan caron

pierre luc carrier

satya ortiz gagne

Marc-Andre Rousseau

David Buckeridge … (see 9 more)

Joumana Ghosn

Yang Zhang

Bernhard Schölkopf

Jian Tang

Chris Pal

Joanna Merckx

2020-10-02

OpenReview.net/Anonymous_Preprint (unknown)

Survey on Applications of Multi-Armed and Contextual Bandits

Djallel Bouneffouf

Charu Aggarwal

In recent years, the multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems a… (see more)nd information retrieval to healthcare and finance. This success is due to its stellar performance combined with attractive properties, such as learning from less feedback. The multiarmed bandit field is currently experiencing a renaissance, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This article aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. Specifically, we introduce a taxonomy of common MAB-based applications and summarize the state-of-the-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this burgeoning field.

2020-07-19

2020 IEEE Congress on Evolutionary Computation (CEC) (published)

doi.org

Chaotic Continual Learning

Touraj Laleh

Mojtaba Faramarzi

Sarath Chandar Anbil Parthipan

Training a deep neural network requires the model to go over training data for several epochs and update network parameters. In continual le… (see more)arning, this process results in catastrophic forgetting which is one of the core issues of this domain. Most proposed approaches for this issue try to compensate for the effects of parameter updates in the batch incremental setup in which the training model visits a lot of samples for several epochs. However, it is not realistic to expect training data will always be fed to model in a batch incremental setup. This paper proposes a chaotic stream learner that mimics the chaotic behavior of biological neurons and does not updates network parameters. In addition, it can work with fewer samples compared to deep learning models on stream learning setup. Our experiments on MNIST, CIFAR10, and Omniglot show that the chaotic stream learner has less catastrophic forgetting by its nature in comparison to a CNN model in continual learning.

2020-07-13

ICML.cc/2020/Workshop/LifelongML (unknown)

COVI White Paper

Hannah Alsdurf

Tristan Deleu

Prateek Gupta

Daphne Ippolito

Richard Janda

Max Jarvie

Tyler J. Kolody

Sekoul Krastev

Tegan Maharaj

Robert Obryk

Dan Pilat

Valerie Pisano

Benjamin Prud'homme

Meng Qu

Nasim Rahaman

Jean-franois Rousseau

abhinav sharma

Brooke Struck … (see 3 more)

Jian Tang

Martin Weiss

Yun William Yu

2020-05-18

ArXiv (preprint)

An Empirical Study of Human Behavioral Agents in Bandits, Contextual Bandits and Reinforcement Learning.

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

Artificial behavioral agents are often evaluated based on their consistent behaviors and performance to take sequential actions in an enviro… (see more)nment to maximize some notion of cumulative reward. However, human decision making in real life usually involves different strategies and behavioral trajectories that lead to the same empirical outcome. Motivated by clinical literature of a wide range of neurological and psychiatric disorders, we propose here a more general and flexible parametric framework for sequential decision making that involves a two-stream reward processing mechanism. We demonstrated that this framework is flexible and unified enough to incorporate a family of problems spanning multi-armed bandits (MAB), contextual bandits (CB) and reinforcement learning (RL), which decompose the sequential decision making process in different levels. Inspired by the known reward processing abnormalities of many mental disorders, our clinically-inspired agents demonstrated interesting behavioral trajectories and comparable performance on simulated tasks with particular reward distributions, a real-world dataset capturing human decision-making in gambling tasks, and the PacMan game across different reward stationarities in a lifelong learning setting.

2020-05-10

(published)

www.semanticscholar.org

Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

2020-05-10

ArXiv (preprint)

Modeling Dialogues with Hashcode Representations: A Nonparametric Approach

Sahil Garg

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… (see more)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.

2020-04-03

AAAI Conference on Artificial Intelligence (published)

doi.org

Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation

Victor Schmidt

Makesh Narsimhan Sreedhar

Mostafa ElAraby

Unpaired Image-to-Image Translation (I2IT) tasks often suffer from lack of data, a problem which self-supervised learning (SSL) has recently… (see more) been very popular and successful at tackling. Leveraging auxiliary tasks such as rotation prediction or generative colorization, SSL can produce better and more robust representations in a low data regime. Training such tasks along an I2IT task is however computationally intractable as model size and the number of task grow. On the other hand, learning sequentially could incur catastrophic forgetting of previously learned tasks. To alleviate this, we introduce Lifelong Self-Supervision (LiSS) as a way to pre-train an I2IT model (e.g., CycleGAN) on a set of self-supervised auxiliary tasks. By keeping an exponential moving average of past encoders and distilling the accumulated knowledge, we are able to maintain the network's validation performance on a number of tasks without any form of replay, parameter isolation or retraining techniques typically used in continual learning. We show that models trained with LiSS perform better on past tasks, while also being more robust than the CycleGAN baseline to color bias and entity entanglement (when two entities are very close).

2020-03-31

ArXiv (preprint)