Irina Rish

Ivan Anokhin

Doctorat - UdeM

Co-superviseur⋅e :

Samira Ebrahimi Kahou

ivan.anokhin@mila.quebec

Doctorat - UdeM

Arjun Ashok

Doctorat

Co-superviseur⋅e :

Alexandre Drouin

arjun.ashok@mila.quebec

Reza Bayat

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Pouya Bashivan

reza.bayat@mila.quebec

rishika.bhagwatkar@mila.quebec

Rishika Bhagwatkar

Maîtrise recherche - UdeM

Collaborateur·rice de recherche - UdeM

connor.brennan@mila.quebec

Ethan Caballero

Doctorat - McGill

Superviseur⋅e principal⋅e :

Blake Richards

ethan.caballero@mila.quebec

paolo.cudrano@mila.quebec

Paolo Cudrano

Collaborateur·rice de recherche - Politecnico di Milano

Mohammad-Javad Darvishi Bayazi

Amin Darabi

Doctorat - UdeM

amin.darabi@mila.quebec

Doctorat - UdeM

mohammad-javad.darvishi-bayasi@mila.quebec

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Sarath Chandar Anbil Parthipan

kshitij.gupta@mila.quebec

Nadhir Hassen

Stagiaire de recherche - UdeM

nadhir.hassen@mila.quebec

Prateek Humane

Collaborateur·rice de recherche

prateek.humane@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Nizar Islah

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Eilif Benjamin Muller

nizar.islah@mila.quebec

Doctorat - UdeM

arnav-kumar.jain@mila.quebec

Collaborateur·rice de recherche

ayush.kaushal@mila.quebec

Arian Khorasani

Maîtrise recherche - UdeM

arian.khorasani@mila.quebec

Neeraj Kumar

Maîtrise professionnelle - UdeM

neeraj.kumar@mila.quebec

gwendolyne.legate@mila.quebec

Gwen Legate

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Eugene Belilovsky

ekaterina.lobacheva@mila.quebec

Kate Lobacheva

Postdoctorat

Superviseur⋅e principal⋅e :

Nicolas Le Roux

Maîtrise recherche - UdeM

sayed.mansouri-tehrani@mila.quebec

juan.mayor-torres@mila.quebec

Juan Manuel Mayor-Torres

Collaborateur·rice de recherche

Visiteur de recherche indépendant

memariaa@mila.quebec

Andrei Mircea Romascanu

Doctorat - UdeM

Maîtrise recherche - UdeM

sparsha.mishra@mila.quebec

Diganta Misra

Maîtrise recherche - UdeM

diganta.misra@mila.quebec

motahareh.pourrahimi@mila.quebec

Remus Mocanu

Maîtrise recherche - UdeM

remus.mocanu@mila.quebec

Timothy Nest

Doctorat - UdeM

Co-superviseur⋅e :

Eilif Benjamin Muller

timothy.nest@mila.quebec

Mohammad Pezeshki

Collaborateur·rice de recherche

Doctorat - McGill

Superviseur⋅e principal⋅e :

Pouya Bashivan

mahta.ramezanian@mila.quebec

Maximilian Puelma Touzel

Collaborateur·rice de recherche

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Guillaume Dumas

Roland Riachi

Collaborateur·rice de recherche - UdeM

roland.riachi@mila.quebec

Matthew Riemer

Doctorat - UdeM

matthew.riemer@mila.quebec

Alexis Roger

Maîtrise recherche - UdeM

alexis.roger@mila.quebec

zahra.sheikhbahaee@mila.quebec

Zahra Sheikhbahaee

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Dumas

vaibhav.singh@mila.quebec

Vaibhav Singh

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Eugene Belilovsky

Gopeshh Subbaraj

Doctorat - UdeM

gopeshh.subbaraj@mila.quebec

Doctorat - UdeM

Co-superviseur⋅e :

Eugene Belilovsky

benjamin.therien@mila.quebec

pascal.tikeng@mila.quebec

Pascal Tikeng Notsawo

Doctorat - UdeM

Co-superviseur⋅e :

Guillaume Dumas

Maîtrise recherche - UdeM

tejas.vaidhya@mila.quebec

Dave Whipps

Maîtrise recherche - UdeM

whippsda@mila.quebec

andrew.williams@mila.quebec

Andrew Williams

Doctorat - UdeM

daria.yasafova@mila.quebec

Daria Yasafova

Stagiaire de recherche - Technical University of Munich

Omar Younis

Stagiaire de recherche - UdeM

omar.younis@mila.quebec

Publications

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… (voir plus) 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 (prépublication)

Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning

Massimo Caccia

Pau Rodriguez

Oleksiy Ostapenko

Fabrice Normandin

Min Lin

Lucas Caccia

Issam Hadj Laradji

Alexande Lacoste

David Vazquez

Laurent Charlin

2020-03-12

ArXiv (prépublication)

Resting-state connectivity stratifies premanifest Huntington’s disease by longitudinal cognitive decline rate

Pablo Polosecki

Eduardo Castro

Dorian Pustina

John H. Warner

Andrew Wood

Cristina Sampaio

Guillermo Cecchi

2020-01-27

Scientific Reports (publié)

doi.org

COVI White Paper-Version 1.1

Hannah Alsdurf

Yoshua Bengio

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 … (voir 3 de plus)

Jian Tang

Martin Weiss

Yun William Yu

The SARS-CoV-2 (Covid-19) pandemic has resulted in significant strain on health care and public health institutions around the world. Contac… (voir plus)t tracing is an essential tool for public health officials and local communities to change the course of the Covid-19 pandemic. Standard manual contact tracing of people infected with Covid-19, while the current gold standard, has significant challenges that limit the ability of public health authorities to minimize community infections. Personalized peer-to-peer contact tracing through the use of mobile applications has the potential to shift the paradigm of Covid-19 community spread. Although some countries have deployed centralized tracking systems through either GPS or Bluetooth, more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or in for-profit corporations. Additionally, machine learning methods can be used to circumvent some of the limitations of standard digital tracing by incorporating many clues (including medical conditions, self-reported symptoms, and numerous encounters with people at different risk levels, for different durations and distances) and their uncertainty into a more graded and precise estimation of infection and contagion risk. The estimated risk can be used to provide early risk awareness, personalized recommendations and relevant information to the user and connect them to health services. Finally, the non-identifying data about these risks can inform detailed epidemiological models trained jointly with the machine learning predictor, and these models can provide statistical evidence for the interaction and importance of different factors involved in the transmission of the disease. They can also be used to monitor, evaluate and optimize different health policy and confinement/deconfinement scenarios according to medical and economic productivity indicators. However, such a strategy based on mobile apps and machine learning should proactively mitigate potential ethical and privacy risks, which could have substantial impacts on society (not only impacts on health but also impacts such as stigmatization and abuse of personal data). Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of ‘COVI,’ a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada. Addendum 2020-07-14: The government of Canada has declined to endorse COVI and will be promoting a different app for decentralized contact tracing. In the interest of preventing fragmentation of the app landscape, COVI will therefore not be deployed to end users. We are currently still in the process of finalizing the project, and plan to release our code and models for academic consumption and to make them accessible to other States should they wish to deploy an app based on or inspired by said code and models. University of Ottawa, Mila, Université de Montréal, The Alan Turing Institute, University of Oxford, University of Pennsylvania, McGill University, Borden Ladner Gervais LLP, The Decision Lab, HEC Montréal, Max Planck Institute, Libéo, University of Toronto. Corresponding author general: richard.janda@mcgill.ca Corresponding author for public health: abhinav.sharma@mcgill.ca Corresponding author for privacy: ywyu@math.toronto.edu Corresponding author for machine learning: yoshua.bengio@mila.quebec Corresponding author for user perspective: brooke@thedecisionlab.com Corresponding author for technical implementation: jean-francois.rousseau@libeo.com 1 ar X iv :2 00 5. 08 50 2v 2 [ cs .C R ] 2 7 Ju l 2 02 0

2020-01-01

(publié)

www.semanticscholar.org

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

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

2020-01-01

HBAI@IJCAI (publié)

doi.org

Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning.

Massimo Caccia

Pau Rodriguez

Oleksiy Ostapenko

Fabrice Normandin

Min Lin

Lucas Caccia

Issam Hadj Laradji

Alexandre Lacoste

David Vazquez

Laurent Charlin

A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework fo… (voir plus)r reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases -- an important component of human decision making which can help us better understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems, as well as various neuropsychiatric conditions associated with disruptions in normal reward processing. From the computational perspective, we observe that the proposed Split-QL model and its clinically inspired variants consistently outperform standard Q-Learning and SARSA methods, as well as recently proposed Double Q-Learning approaches, on simulated tasks with particular reward distributions, a real-world dataset capturing human decision-making in gambling tasks, and the Pac-Man game in a lifelong learning setting across different reward stationarities.

2020-01-01

AAMAS (publié)

Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement … (voir plus)learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. For the AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributions and real-world human decision making gambling tasks. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.

2019-10-02

NeurIPS.cc/2019/Workshop/Neuro_AI (poster)

openreview.net

Continual Learning with Self-Organizing Maps

Pouya Bashivan

Martin Schrimpf

Robert Ajemian

Matthew D Riemer

Yuhai Tu

Despite remarkable successes achieved by modern neural networks in a wide range of applications, these networks perform best in domain-speci… (voir plus)fic stationary environments where they are trained only once on large-scale controlled data repositories. When exposed to non-stationary learning environments, current neural networks tend to forget what they had previously learned, a phenomena known as catastrophic forgetting. Most previous approaches to this problem rely on memory replay buffers which store samples from previously learned tasks, and use them to regularize the learning on new ones. This approach suffers from the important disadvantage of not scaling well to real-life problems in which the memory requirements become enormous. We propose a memoryless method that combines standard supervised neural networks with self-organizing maps to solve the continual learning problem. The role of the self-organizing map is to adaptively cluster the inputs into appropriate task contexts - without explicit labels - and allocate network resources accordingly. Thus, it selectively routes the inputs in accord with previous experience, ensuring that past learning is maintained and does not interfere with current learning. Out method is intuitive, memoryless, and performs on par with current state-of-the-art approaches on standard benchmarks.

2019-04-19

ArXiv (prépublication)

A Survey on Practical Applications of Multi-Armed and Contextual Bandits

Djallel Bouneffouf

In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and i… (voir plus)nformation retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback. The multi-armed bandit field is currently flourishing, 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 state-of-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this exciting and fast-growing field.

2019-04-02

ArXiv (prépublication)

Predicting conversion to psychosis in clinical high risk patients using resting-state functional MRI features

Jolie Mcdonnell

W. Hord

Jenna Reinen

Pablo Polosecki

Guillermo Cecchi

Recent progress in artificial intelligence provides researchers with a powerful set of machine learning tools for analyzing brain imaging da… (voir plus)ta. In this work, we explore a variety of classification algorithms and functional network features derived from resting-state fMRI data collected from clinical high-risk (prodromal schizophrenia) patients and controls, trying to identify features predictive of conversion to psychosis among a subset of CHR patients. While there are many existing studies suggesting that functional network features can be highly discriminative of schizophrenia when analyzing fMRI of patients suffering from the disease vs controls, few studies attempt to explore a similar approach to actual prediction of future psychosis development ahead of time, in the prodromal stage. Our preliminary results demonstrate the potential of fMRI functional network features to predict the conversion to psychosis in CHR patients. However, given the high variance of our results across different classifiers and subsets of data, a more extensive empirical investigation is required to reach more robust conclusions.

2019-02-16

Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging (publié)

doi.org

Learning Brain Dynamics from Calcium Imaging with Coupled van der Pol and LSTM

Germán Abrevaya