Doina Precup

busra-tugce.gurbuz@mila.quebec

Jesse Farebrother

Doctorat - McGill

Superviseur⋅e principal⋅e :

Marc Gendron-Bellemare

Doctorat - McGill

Superviseur⋅e principal⋅e :

Eilif B. Muller

Mohammad Sami Nur Islam Islam

Jonathan Hu

Maîtrise recherche - McGill

jonathan.hu@mila.quebec

Howard Huang

Doctorat - McGill

huanghow@mila.quebec

Maîtrise recherche - McGill

mohammad-sami-nur.islam@mila.quebec

Hangzhan Jin

Doctorat - Polytechnique

jinhangzhan@gmail.com

flemming.kondrup@mila.quebec

Flemming Kondrup

Postdoctorat - McGill

Jonathan Lebensold

Collaborateur·rice alumni - McGill

Collaborateur·rice alumni - McGill

jeremy.lucas@mila.quebec

Ray Luo

Doctorat - McGill

Superviseur⋅e principal⋅e :

Xujie Si

luo.ziyan@mila.quebec

Sicheng Lyu

Maîtrise recherche - McGill

sicheng.lyu@mila.quebec

G McCracken

Doctorat - McGill

mccrackg@mila.quebec

shahrad.mohammadzadeh@mila.quebec

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Reihaneh Rabbany

padideh.nouri@mila.quebec

Gabriela Moisescu-Pareja

Collaborateur·rice de recherche - McGill

Co-superviseur⋅e :

Irina Rish

moisescg@mila.quebec

Padideh Nouri

Doctorat - UdeM

Doctorat - McGill

Co-superviseur⋅e :

Stagiaire de recherche - McGill

william.potter@mila.quebec

manoosh.samiei@mila.quebec

Nate Rahn

Doctorat - McGill

Superviseur⋅e principal⋅e :

Marc Gendron-Bellemare

nathan.rahn@mila.quebec

Mandana Samiei

Doctorat - McGill

Co-superviseur⋅e :

Blake Richards

samiemandana@gmail.com

Doctorat - McGill

Co-superviseur⋅e :

Paul Masset

Doctorat - McGill

Nishanth Anand Vemgal

Doctorat - McGill

Doctorat - McGill

Co-superviseur⋅e :

Samira Ebrahimi Kahou

Stagiaire de recherche - McGill

guangyuan.wang@mila.quebec

Zihan Wang

Doctorat - McGill

zihan.wang@mila.quebec

shuyuan.zhang@mila.quebec

Steve Wen

Maîtrise recherche - McGill

Co-superviseur⋅e :

Gregory Dudek

steve.wen@mila.quebec

Zijing Wu

Doctorat - McGill

Superviseur⋅e principal⋅e :

Paul Masset

zijing.wu@mila.quebec

Doctorat - McGill

Harry Zhao

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Billets de blogue

Generic thumbnail for Mila Blog articles.

22 février 2024

Skipper : combiner l’abstraction spatiale et temporelle afin d’améliorer la généralisation

par

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

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Publications

Independently Controllable Factors

Valentin Thomas

Marie-Jean Meurs

It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it rema… (voir plus)ins an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.

2016-12-31

arXiv (prépublication)

Independently Controllable Features

Valentin Thomas

2016-12-31

CoRR (publié)

A Matrix Splitting Perspective on Planning with Options

Pierre-Luc Bacon

2016-12-02

ArXiv (prépublication)

Smart Classifier Selection for Activity Recognition on Wearable Devices

Negar Ghourchian

Activity recognition is a key component of human-machine interaction applications. Information obtained from sensors in smart wearable devic… (voir plus)es is especially valuable, because these devices have become ubiquitous, and they record large amounts of data. Machine learning algorithms can then be used to process this data. However, wearable devices impose restrictions in terms of computation and energy resources, which need to be taken into account by a learning algorithm. We propose to use a real-time learning approach, which interactively determines the most effective set of modalities (or features) for classification, given the task at hand. Our algorithm optimizes sensor selection, in order to consume less power, while still maintaining good accuracy in classifying sequences of activities. Performance on a large, noisy dataset including four different sensing modalities shows that this is a promising approach.

2016-11-22

International Conference on Pattern Recognition Applications and Methods (publié)

Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms

Kian Kenyon-Dean

Jackie CK Cheung

Verb Phrase Ellipsis (VPE) is an anaphoric construction in which a verb phrase has been elided. It occurs frequently in dialogue and informa… (voir plus)l conversational settings, but despite its evident impact on event coreference resolution and extraction, there has been relatively little work on computational methods for identifying and resolving VPE. Here, we present a novel approach to detecting and resolving VPE by using supervised discriminative machine learning techniques trained on features extracted from an automatically parsed, publicly available dataset. Our approach yields state-of-the-art results for VPE detection by improving F1 score by over 11%; additionally, we explore an approach to antecedent identiﬁ-cation that uses the Margin-Infused-Relaxed-Algorithm, which shows promising results.

2016-10-31

Conference on Empirical Methods in Natural Language Processing (publié)

Prediction of Cell Type Specific Transcription Factor Binding Site Occupancy

Faizy Ahsan

Mathieu Blanchette

2016-10-01

ACM International Conference on Bioinformatics, Computational Biology and Biomedicine (publié)

Editorial on Special Issue on Probabilistic Models for Biomedical Image Analysis.

Tal Arbel

Manuel Jorge Cardoso

William M. Wells III

Albert C. S. Chung

2015-12-31

Computer Vision and Image Understanding (publié)

Bayesian and grAphical Models for Biomedical Imaging

M. Jorge Cardoso

Ivor J. A. Simpson

Arbel, Tal

Annemie Ribbens

2013-12-31

Lecture Notes in Computer Science (publié)

Assessing Generalization in TD methods for Deep Reinforcement Learning

Emmanuel Bengio

Joelle Pineau

2010-12-31

International Conference on Computational Intelligence, Modelling and Simulation (publié)

openreview.net

Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces

Thang Doan

We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional… (voir plus) embedding of the policy on a reproducing kernel Hilbert space (RKHS). The usage of RKHS based methods allows us to derive strong theoretical guarantees on the expected return of the reconstructed policy. Such guarantees are typically lacking in black-box models, but are very desirable in tasks requiring stability. We conduct several experiments on classic RL domains. The results confirm that the policies can be robustly embedded in a low-dimensional space while the embedded policy incurs almost no decrease in return.

2010-12-31

International Conference on Computational Intelligence, Modelling and Simulation (publié)