Doina Precup

Samin Yeasar Arnob

PhD - McGill University

arnobsam@mila.quebec

sumana.basu@mail.mcgill.ca

Sumana Basu

Collaborating Alumni - McGill University

Co-supervisor :

Adriana Romero Soriano

Master's Research - McGill University

gunbir-singh.baveja@mila.quebec

Collaborating Alumni - McGill University

cheluver@mila.quebec

Raymond Chua

PhD - McGill University

Co-supervisor :

PhD - McGill University

Principal supervisor :

David Meger

chungwes@mila.quebec

Jonathan Colaço Carr

Master's Research - McGill University

Principal supervisor :

Prakash Panangaden

jonathan.colaco-carr@mila.quebec

Élodie Coté-Gauthier

Master's Research - McGill University

Co-supervisor :

Isabeau Prémont-Schwarz

elodie.cote-gauthier@mila.quebec

Franco Del Balso

Master's Research - McGill University

franco.delbalso@mila.quebec

busra-tugce.gurbuz@mila.quebec

Jesse Farebrother

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

PhD - McGill University

Principal supervisor :

Eilif B. Muller

Mohammad Sami Nur Islam Islam

Jonathan Hu

Master's Research - McGill University

jonathan.hu@mila.quebec

Howard Huang

PhD - McGill University

huanghow@mila.quebec

Master's Research - McGill University

mohammad-sami-nur.islam@mila.quebec

Hangzhan Jin

PhD - Polytechnique Montréal

jinhangzhan@gmail.com

flemming.kondrup@mila.quebec

Flemming Kondrup

Postdoctorate - McGill University

Jonathan Lebensold

Collaborating Alumni - McGill University

Collaborating Alumni - McGill University

jeremy.lucas@mila.quebec

Ray Luo

PhD - McGill University

Principal supervisor :

Xujie Si

luo.ziyan@mila.quebec

Sicheng Lyu

Master's Research - McGill University

sicheng.lyu@mila.quebec

G McCracken

PhD - McGill University

mccrackg@mila.quebec

shahrad.mohammadzadeh@mila.quebec

Shahrad Mohammadzadeh

Master's Research - McGill University

Principal supervisor :

Reihaneh Rabbany

padideh.nouri@mila.quebec

Gabriela Moisescu-Pareja

Collaborating researcher - McGill University

Co-supervisor :

Irina Rish

moisescg@mila.quebec

Padideh Nouri

PhD - Université de Montréal

PhD - McGill University

Co-supervisor :

Research Intern - McGill University

william.potter@mila.quebec

manoosh.samiei@mila.quebec

Nate Rahn

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

nathan.rahn@mila.quebec

Mandana Samiei

PhD - McGill University

Co-supervisor :

Blake Richards

samiemandana@gmail.com

PhD - McGill University

Co-supervisor :

Paul Masset

PhD - McGill University

Nishanth Anand Vemgal

PhD - McGill University

PhD - McGill University

Co-supervisor :

Samira Ebrahimi Kahou

Zihan Wang

PhD - McGill University

zihan.wang@mila.quebec

guangyuan.wang@mila.quebec

Guangyuan Wang

Research Intern - McGill University

Steve Wen

Master's Research - McGill University

Co-supervisor :

Gregory Dudek

steve.wen@mila.quebec

Zijing Wu

PhD - McGill University

Principal supervisor :

Paul Masset

zijing.wu@mila.quebec

PhD - McGill University

shuyuan.zhang@mila.quebec

Harry Zhao

Collaborating Alumni - McGill University

Co-supervisor :

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

Read the article

Publications

Learning Modular Safe Policies in the Bandit Setting with Application to Adaptive Clinical Trials

Hossein Aboutalebi

Tibor Schuster

The stochastic multi-armed bandit problem is a well-known model for studying the exploration-exploitation trade-off. It has significant poss… (see more)ible applications in adaptive clinical trials, which allow for dynamic changes in the treatment allocation probabilities of patients. However, most bandit learning algorithms are designed with the goal of minimizing the expected regret. While this approach is useful in many areas, in clinical trials, it can be sensitive to outlier data, especially when the sample size is small. In this paper, we define and study a new robustness criterion for bandit problems. Specifically, we consider optimizing a function of the distribution of returns as a regret measure. This provides practitioners more flexibility to define an appropriate regret measure. The learning algorithm we propose to solve this type of problem is a modification of the BESA algorithm [Baransi et al., 2014], which considers a more general version of regret. We present a regret bound for our approach and evaluate it empirically both on synthetic problems as well as on a dataset from the clinical trial literature. Our approach compares favorably to a suite of standard bandit algorithms.

2019-03-03

ArXiv (preprint)

Prediction of Progression in Multiple Sclerosis Patients

Adrian Tousignant

Paul Lemaitre

Douglas Arnold

Tal Arbel

We present the first automatic end-to-end deep learning framework for the prediction of future patient disability progression (one year from… (see more) baseline) based on multi-modal brain Magnetic Resonance Images (MRI) of patients with Multiple Sclerosis (MS). The model uses parallel convolutional pathways, an idea introduced by the popular Inception net and is trained and tested on two large proprietary, multi-scanner, multi-center, clinical trial datasets of patients with Relapsing-Remitting Multiple Sclerosis (RRMS). Experiments on 465 patients on the placebo arms of the trials indicate that the model can accurately predict future disease progression, measured by a sustained increase in the extended disability status scale (EDSS) score over time. Using only the multi-modal MRI provided at baseline, the model achieves an AUC of 0.66 +- 0.055. However, when supplemental lesion label masks are provided as inputs as well, the AUC increases to 0.701 +- 0.027. Furthermore, we demonstrate that uncertainty estimates based on Monte Carlo dropout sample variance correlate with errors made by the model. Clinicians provided with the predictions computed by the model can therefore use the associated uncertainty estimates to assess which scans require further examination.

2019-02-27

MIDL.io/2019/Conference (accepted)

openreview.net

The Termination Critic

Anna Harutyunyan

Will Dabney

Diana Borsa

Nicolas Heess

Remi Munos

In this work, we consider the problem of autonomously discovering behavioral abstractions, or options, for reinforcement learning agents. We… (see more) propose an algorithm that focuses on the termination function, as opposed to - as is common - the policy. The termination function is usually trained to optimize a control objective: an option ought to terminate if another has better value. We offer a different, information-theoretic perspective, and propose that terminations should focus instead on the compressibility of the option’s encoding - arguably a key reason for using abstractions.To achieve this algorithmically, we leverage the classical options framework, and learn the option transition model as a “critic” for the termination function. Using this model, we derive gradients that optimize the desired criteria. We show that the resulting options are non-trivial, intuitively meaningful, and useful for learning.

2019-02-25

ArXiv (preprint)

The Impact of Time Interval between Extubation and Reintubation on Death or Bronchopulmonary Dysplasia in Extremely Preterm Infants

Wissam Shalish

Lara Kanbar

Lajos Kovacs

Sanjay Chawla

Martin Keszler

Smita Rao

Bogdan Panaitescu

Alyse Laliberte

Karen Brown

Robert E. Kearney

Guilherme M. Sant'Anna

2019-01-31

The Journal of Pediatrics (published)

doi.org

1. Searching for Big-Oh in the Data: Inferring Asymptotic Complexity from Experiments

Catherine McGeoch

Peter Sanders 0001

Rudolf Fleischer

Paul R. Cohen

2018-12-31

(published)

www.semanticscholar.org

Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks

Sitao Luan

Mingde Zhao

Xiao-Wen Chang

Recently, neural network based approaches have achieved significant improvement for solving large, complex, graph-structured problems. Howev… (see more)er, their bottlenecks still need to be addressed, and the advantages of multi-scale information and deep architectures have not been sufficiently exploited. In this paper, we theoretically analyze how existing Graph Convolutional Networks (GCNs) have limited expressive power due to the constraint of the activation functions and their architectures. We generalize spectral graph convolution and deep GCN in block Krylov subspace forms and devise two architectures, both with the potential to be scaled deeper but each making use of the multi-scale information in different ways. We further show that the equivalence of these two architectures can be established under certain conditions. On several node classification tasks, with or without the help of validation, the two new architectures achieve better performance compared to many state-of-the-art methods.

2018-12-31

NeurIPS (published)

doi.org

Community size effect in artificial learning systems

Olivier Tieleman

Angeliki Lazaridou

Shibl Mourad

Charles Blundell

Motivated by theories of language and communication that explain why communities with large numbers of speakers have, on average, simpler la… (see more)nguages with more regularity, we cast the representation learning problem in terms of learning to communicate . Our starting point sees the traditional autoencoder setup as a single encoder with a ﬁxed decoder partner that must learn to communicate. Generalizing from there, we introduce community -based autoencoders in which multiple encoders and decoders collectively learn representations by being randomly paired up on successive training iterations. We ﬁnd that increasing community sizes reduce idiosyncrasies in the learned codes, resulting in representations that better encode concept categories and correlate with human feature norms.

2018-12-31

ViGIL@NeurIPS (published)

dblp.uni-trier.de

Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning ( Supplementary Material ) A Proofs

More precisely, the WFA A = (α, {A}σ∈Σ,Ω) with n states and the linear 2-RNN M = (α,A,Ω) with n hidden units, where A ∈ Rn×Σ×n … (see more)is defined by A:,σ,: = A for all σ ∈ Σ, are such that fA(σ1σ2 · · ·σk) = fM (x1,x2, · · · ,xk) for all sequences of input symbols σ1, · · · , σk ∈ Σ, where for each i ∈ [k] the input vector xi ∈ RΣ is the one-hot encoding of the symbol σi. Proof. We first show by induction on k that, for any sequence σ1 · · ·σk ∈ Σ∗, the hidden state hk computed by M (see Eq. (1)) on the corresponding one-hot encoded sequence x1, · · · ,xk ∈ R satisfies hk = (A1 · · ·Ak )>α. The case k = 0 is immediate. Suppose the result true for sequences of length up to k. One can check easily check that A •2 xi = Ai for any index i. Using the induction hypothesis it then follows that hk+1 = A •1 hk •2 xk+1 = Ak+1 •1 hk = (Ak+1)hk = (Aσk+1)>(Aσ1 · · ·Ak )>α = (A1 · · ·Aσk+1)>α.

2018-12-31

(published)

www.semanticscholar.org

Data-driven Chance Constrained Programming based Electric Vehicle Penetration Analysis

Di Wu

Tracy Can Cui

Benoit Boulet

Transportation electrification has been growing rapidly in recent years. The adoption of electric vehicles (EVs) could help to release the d… (see more)ependency on oil and reduce greenhouse gas emission. However, the increasing EV adoption will also impose a high demand on the power grid and may jeopardize the grid network infrastructures. For certain high EV penetration areas, the EV charging demand may lead to transformer overloading at peak hours which makes the maximal EV penetration analysis an urgent problem to solve. This paper proposes a data-driven chance constrained programming based framework for maximal EV penetration analysis. Simulation results are presented for a real-world neighborhood level network. The proposed framework could serve as a guidance for utility companies to schedule infrastructure upgrades.

2018-12-31

(published)

www.semanticscholar.org

An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation

Vincent Michalski

Vikram Voleti

Samira Ebrahimi Kahou

Anthony Ortiz

Pascal Vincent

Chris Pal

Batch normalization has been widely used to improve optimization in deep neural networks. While the uncertainty in batch statistics can act … (see more)as a regularizer, using these dataset statistics specific to the training set impairs generalization in certain tasks. Recently, alternative methods for normalizing feature activations in neural networks have been proposed. Among them, group normalization has been shown to yield similar, in some domains even superior performance to batch normalization. All these methods utilize a learned affine transformation after the normalization operation to increase representational power. Methods used in conditional computation define the parameters of these transformations as learnable functions of conditioning information. In this work, we study whether and where the conditional formulation of group normalization can improve generalization compared to conditional batch normalization. We evaluate performances on the tasks of visual question answering, few-shot learning, and conditional image generation.

2018-12-31

arXiv (preprint)

doi.org

Hindsight Credit Assignment

Anna Harutyunyan

Will Dabney

Thomas Mesnard

Mohammad Gheshlaghi Azar

Bilal Piot

Nicolas Heess

Hado van Hasselt

Greg Wayne

Satinder Singh

Remi Munos

2018-12-31

Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (published)

Learning Reliable Policies in the Bandit Setting with Application to Adaptive Clinical Trials

Hossein Aboutalebi