Aaron Courville

sarvjeet-singh.ghotra@mila.quebec

Juan Duque

PhD - Université de Montréal

juan.duque@mila.quebec

PhD - Université de Montréal

udaykaran.kapur@mila.quebec

Arian Hosseini

PhD - Université de Montréal

Uday Kapur

PhD - Université de Montréal

Amr Khalifa

PhD - Université de Montréal

amr-khalifa.alshaykh@mila.quebec

samuel.lavoie@mila.quebec

Samuel Lavoie

PhD - Université de Montréal

Zhixuan Lin

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

Simon Lacoste-Julien

alan.milligan@mila.quebec

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

andjela.mladenovic@mila.quebec

mohammed.muqeeth@mila.quebec

Muqeeth Mohammed

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Rishabh Agarwal

morgane.moss@mila.quebec

Andrei Nicolicioiu

PhD - Université de Montréal

andrei.nicolicioiu@gmail.com

michell.payanoperez@mila.quebec

Michael Noukhovitch

PhD - Université de Montréal

Johan Samir Obando Ceron

PhD - Université de Montréal

Co-supervisor :

Pablo Samuel Castro

johan.ceron@mila.quebec

PhD - Université de Montréal

Dereck Piché

PhD - Université de Montréal

pichedereck@gmail.com

khaled.rouissi@mila.quebec

Khaled Rouissi

Master's Research - Université de Montréal

Esra'a Saleh

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

esraa.saleh@mila.quebec

Vedant Shah

PhD - Université de Montréal

vedant.shah@mila.quebec

PhD - Université de Montréal

tsirigoc@mila.quebec

colin.yiyang-lu@mila.quebec

Yusong Wu

PhD - Université de Montréal

Principal supervisor :

Anna (Cheng-Zhi) Huang

wu.yusong@mila.quebec

Colin Yiyang Lu

PhD - Université de Montréal

Sujin yun

PhD - Université de Montréal

sujin.yun@mila.quebec

xiaofeng.zhang@mila.quebec

Xiaofeng Zhang

PhD - Université de Montréal

Publications

Neural Approximate Sufficient Statistics for Implicit Models

Yanzhi Chen

Dinghuai Zhang

Michael U. Gutmann

Zhanxing Zhu

We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation… (see more) of the likelihood function is intractable, but sampling data from the model is possible. The idea is to frame the task of constructing sufficient statistics as learning mutual information maximizing representations of the data with the help of deep neural networks. The infomax learning procedure does not need to estimate any density or density ratio. We apply our approach to both traditional approximate Bayesian computation and recent neural likelihood methods, boosting their performance on a range of tasks.

2021-05-02

International Conference on Learning Representations (Spotlight)

Systematic Generalisation with Group Invariant Predictions

Faruk Ahmed

Yoshua Bengio

Harm van Seijen

2021-05-02

International Conference on Learning Representations (Spotlight)

Touch-based Curiosity for Sparse-Reward Tasks

Sai Rajeswar

Cyril Ibrahim

Nitin Surya

Florian Golemo

David Vázquez

Pedro O. Pinheiro

2021-03-31

ArXiv (preprint)

Pretraining Reward-Free Representations for Data-Efficient Reinforcement Learning

Philip Bachman

2021-03-08

International Conference on Learning Representations (unknown)

DATA-EFFICIENT REINFORCEMENT LEARNING

R Devon Hjelm

Philip Bachman

Data efficiency poses a major challenge for deep reinforcement learning. We approach this issue from the perspective of self-supervised repr… (see more)esentation learning, leveraging reward-free exploratory data to pretrain encoder networks. We employ a novel combination of latent dynamics modelling and goal-reaching objectives, which exploit the inherent structure of data in reinforcement learning. We demonstrate that our method scales well with network capacity and pretraining data. When evaluated on the Atari 100k data-efficiency benchmark, our approach significantly outperforms previous methods combining unsupervised pretraining with task-specific finetuning, and approaches human-level performance.

2020-12-31

(published)

www.semanticscholar.org

Emergent Communication under Competition

Michael Noukhovitch

Travis LaCroix

Angeliki Lazaridou

2020-12-31

AAMAS (published)

Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond

2020-12-31

arXiv.org (preprint)

dblp.uni-trier.de

Bijective-Contrastive Estimation

Jae Hyun Lim

Chin-wei Huang

Christopher Pal

In this work, we propose Bijective-Contrastive Estimation (BCE), a classification-based learning criterion for energy-based models. We gener… (see more)ate a collection of contrasting distributions using bijections, and solve all the classification problems between the original data distribution and the distributions induced by the bijections using a classifier parameterized by an energy model. We show that if the classification objective is minimized, the energy function will uniquely recover the data density up to a normalizing constant. This has the benefit of not having to explicitly specify a contrasting distribution, like noise contrastive estimation. Experimentally, we demonstrate that the proposed method works well on 2D synthetic datasets. We discuss the difficulty in high dimensional cases, and propose potential directions to explore for future work.

2020-12-20

Symposium on Advances in Approximate Bayesian Inference (accepted)

Unsupervised Learning of Dense Visual Representations

Pedro O. Pinheiro

Amjad Almahairi

Ryan Y. Benmalek

Florian Golemo

Contrastive self-supervised learning has emerged as a promising approach to unsupervised visual representation learning. In general, these m… (see more)ethods learn global (image-level) representations that are invariant to different views (i.e., compositions of data augmentation) of the same image. However, many visual understanding tasks require dense (pixel-level) representations. In this paper, we propose View-Agnostic Dense Representation (VADeR) for unsupervised learning of dense representations. VADeR learns pixelwise representations by forcing local features to remain constant over different viewing conditions. Specifically, this is achieved through pixel-level contrastive learning: matching features (that is, features that describes the same location of the scene on different views) should be close in an embedding space, while non-matching features should be apart. VADeR provides a natural representation for dense prediction tasks and transfers well to downstream tasks. Our method outperforms ImageNet supervised pretraining (and strong unsupervised baselines) in multiple dense prediction tasks.

2020-12-06

Conference on Neural Information Processing Systems (Accept (poster))

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

Jae Hyun Lim

Christopher Pal

Chin-wei Huang

Entropy is ubiquitous in machine learning, but it is in general intractable to compute the entropy of the distribution of an arbitrary conti… (see more)nuous random variable. In this paper, we propose the amortized residual denoising autoencoder (AR-DAE) to approximate the gradient of the log density function, which can be used to estimate the gradient of entropy. Amortization allows us to significantly reduce the error of the gradient approximator by approaching asymptotic optimality of a regular DAE, in which case the estimation is in theory unbiased. We conduct theoretical and experimental analyses on the approximation error of the proposed method, as well as extensive studies on heuristics to ensure its robustness. Finally, using the proposed gradient approximator to estimate the gradient of entropy, we demonstrate state-of-the-art performance on density estimation with variational autoencoders and continuous control with soft actor-critic.

2020-11-20

Proceedings of the 37th International Conference on Machine Learning (published)

proceedings.mlr.press

Countering Language Drift with Seeded Iterated Learning

Yuchen Lu

Soumye Singhal

Florian Strub

Olivier Pietquin

Pretraining on human corpus and then finetuning in a simulator has become a standard pipeline for training a goal-oriented dialogue agent. N… (see more)evertheless, as soon as the agents are finetuned to maximize task completion, they suffer from the so-called language drift phenomenon: they slowly lose syntactic and semantic properties of language as they only focus on solving the task. In this paper, we propose a generic approach to counter language drift called Seeded iterated learning (SIL). We periodically refine a pretrained student agent by imitating data sampled from a newly generated teacher agent. At each time step, the teacher is created by copying the student agent, before being finetuned to maximize task completion. SIL does not require external syntactic constraint nor semantic knowledge, making it a valuable task-agnostic finetuning protocol. We evaluate SIL in a toy-setting Lewis Game, and then scale it up to the translation game with natural language. In both settings, SIL helps counter language drift as well as it improves the task completion compared to baselines.

2020-11-20

Proceedings of the 37th International Conference on Machine Learning (published)

proceedings.mlr.press

Recursive Top-Down Production for Sentence Generation with Latent Trees

Timothy J. O'Donnell

We model the recursive production property of context-free grammars for natural and synthetic languages. To this end, we present a dynamic p… (see more)rogramming algorithm that marginalises over latent binary tree structures with

2020-10-31

Findings of the Association for Computational Linguistics: EMNLP 2020 (published)