Irina Rish

sayed.mansouri-tehrani@mila.quebec

Amin Darabi

Doctorat - Université de Montréal

amin.darabi@mila.quebec

Amin Memarian

Visiteur de recherche indépendant

memariaa@mila.quebec

Amin Mansouri

Maîtrise recherche - Université de Montréal

andrew.williams@mila.quebec

Andrei Mircea Romascanu

Doctorat - Université de Montréal

Doctorat - Université de Montréal

arian.khorasani@mila.quebec

Arian Khorasani

Maîtrise recherche - Université de Montréal

arnav-kumar.jain@mila.quebec

Arjun Ashok

Doctorat

Co-superviseur⋅e :

Alexandre Drouin

arjun.ashok@mila.quebec

Doctorat - Université de Montréal

Doctorat - Université de Montréal

Co-superviseur⋅e :

Collaborateur·rice de recherche

ayush.kaushal@mila.quebec

Benjamin Therien

Doctorat - Université de Montréal

Co-superviseur⋅e :

Eugene Belilovsky

benjamin.therien@mila.quebec

Collaborateur·rice de recherche - Université de Montréal

connor.brennan@mila.quebec

Daria Yasafova

Stagiaire de recherche - Technical University of Munich

daria.yasafova@mila.quebec

Dave Whipps

Maîtrise recherche - Université de Montréal

whippsda@mila.quebec

diganta.misra@mila.quebec

Diganta Misra

Maîtrise recherche - Université de Montréal

Postdoctorat

Superviseur⋅e principal⋅e :

Nicolas Le Roux

ekaterina.lobacheva@mila.quebec

Doctorat - McGill University

Superviseur⋅e principal⋅e :

Blake Richards

ethan.caballero@mila.quebec

george.adamopoulos@mila.quebec

George Adamopoulos

Stagiaire de recherche

gopeshh.subbaraj@mila.quebec

Germán Abrevaya

Visiteur de recherche indépendant - Université de Montréal

Co-superviseur⋅e :

Doctorat - Université de Montréal

gwendolyne.legate@mila.quebec

Gwen Legate

Doctorat - Concordia University

Superviseur⋅e principal⋅e :

Eugene Belilovsky

Ivan Anokhin

Doctorat - Université de Montréal

Co-superviseur⋅e :

Samira Ebrahimi Kahou

ivan.anokhin@mila.quebec

juan.mayor-torres@mila.quebec

Juan Manuel Mayor-Torres

Collaborateur·rice de recherche

Collaborateur·rice alumni - Université de Montréal

Co-superviseur⋅e :

Sarath Chandar Anbil Parthipan

kshitij.gupta@mila.quebec

Mahta Ramezanian

Maîtrise recherche - Université de Montréal

Co-superviseur⋅e :

Guillaume Dumas

mahta.ramezanian@mila.quebec

Matthew Riemer

Doctorat - Université de Montréal

matthew.riemer@mila.quebec

Maximilian Puelma Touzel

Collaborateur·rice de recherche

Doctorat - Université de Montréal

arefinmr@mila.quebec

Mohammad Pezeshki

Collaborateur·rice de recherche

pezeshki@mila.quebec

Mohammad-Javad Darvishi Bayazi

Doctorat - Université de Montréal

mohammad-javad.darvishi-bayasi@mila.quebec

Doctorat - Université de Montréal

faramarm@mila.quebec

Motahareh Pourrahimi

Doctorat - McGill University

Superviseur⋅e principal⋅e :

Pouya Bashivan

motahareh.pourrahimi@mila.quebec

nadhir.hassen@mila.quebec

Nadhir Hassen

Stagiaire de recherche - Université de Montréal

Neeraj Kumar

Maîtrise professionnelle - Université de Montréal

neeraj.kumar@mila.quebec

Nizar Islah

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Eilif Benjamin Muller

nizar.islah@mila.quebec

paolo.cudrano@mila.quebec

Omar Younis

Stagiaire de recherche - Université de Montréal

omar.younis@mila.quebec

Collaborateur·rice de recherche - Politecnico di Milano

pascal.tikeng@mila.quebec

Pascal Tikeng Notsawo

Doctorat - Université de Montréal

Co-superviseur⋅e :

Guillaume Dumas

Collaborateur·rice de recherche

prateek.humane@mila.quebec

Maîtrise recherche - Université de Montréal

remus.mocanu@mila.quebec

Reza Bayat

Maîtrise recherche - Université de Montréal

Co-superviseur⋅e :

Pouya Bashivan

reza.bayat@mila.quebec

rishika.bhagwatkar@mila.quebec

Rishika Bhagwatkar

Maîtrise recherche - Université de Montréal

Collaborateur·rice de recherche - Université de Montréal

roland.riachi@mila.quebec

Simon Dufort-Labbé

Doctorat - Université de Montréal

simon.dufort-labbe@mila.quebec

Sparsha Mishra

Maîtrise recherche - Université de Montréal

sparsha.mishra@mila.quebec

Tejas Vaidhya

Maîtrise recherche - Université de Montréal

tejas.vaidhya@mila.quebec

Doctorat - Université de Montréal

Co-superviseur⋅e :

Eilif Benjamin Muller

timothy.nest@mila.quebec

Vaibhav Singh

Doctorat - Concordia University

Superviseur⋅e principal⋅e :

Eugene Belilovsky

vaibhav.singh@mila.quebec

Zahra Sheikhbahaee

Postdoctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Guillaume Dumas

zahra.sheikhbahaee@mila.quebec

Publications

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

Aleksandr Y. Aravkin

Guillermo Cecchi

James Kozloski

Pablo Polosecki

Peng Zheng

Silvina Ponce Dawson

Juliana Y. Rhee

David Daniel Cox

Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems. In this paper, we focus on brain calc… (voir plus)ium imaging (CaI) of different organisms (zebrafish and rat), aiming to build a model of joint activation dynamics in large neuronal populations, including the whole brain of zebrafish. We propose a new approach for capturing dynamics of temporal SVD components that uses the coupled (multivariate) van der Pol (VDP) oscillator, a nonlinear ordinary differential equation (ODE) model describing neural activity, with a new parameter estimation technique that combines variable projection optimization and stochastic search. We show that the approach successfully handles nonlinearities and hidden state variables in the coupled VDP. The approach is accurate, achieving 0.82 to 0.94 correlation between the actual and model-generated components, and interpretable, as VDP’s coupling matrix reveals anatomically meaningful positive (excitatory) and negative (inhibitory) interactions across different brain subsystems corresponding to spatial SVD components. Moreover, VDP is comparable to (or sometimes better than) recurrent neural networks (LSTM) for (short-term) prediction of future brain activity; VDP needs less parameters to train, which was a plus on our small training data. Finally, the overall best predictive method, greatly outperforming both VDP and LSTM in shortand long-term predicitve settings on both datasets, was the new hybrid VDP-LSTM approach that used VDP to simulate large domain-specific dataset for LSTM pretraining; note that simple LSTM data-augmentation via noisy versions of training data was much less effective.

2019-01-01

(publié)

www.semanticscholar.org

Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference

Matthew D Riemer

Ignacio Cases

Robert Ajemian

Miao Liu