Aaron Courville

Adrien Ali Taiga

Doctorat - UdeM

Co-superviseur⋅e :

Anirudh Buvanesh

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Laurent Charlin

anirudb1102@gmail.com

Baccalauréat - UdeM

Maîtrise recherche - Université de Montréal

Doctorat - UdeM

Maîtrise recherche - UdeM

Juan Duque

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Amr Khalifa

Doctorat - UdeM

andrei.nicolicioiu@gmail.com

Samuel Lavoie

Doctorat - UdeM

Zhixuan Lin

Doctorat - UdeM

Stagiaire de recherche - Ghent University

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Rishabh Agarwal

Andrei Nicolicioiu

Doctorat - UdeM

Site web

Evgenii Nikishin

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Johan Samir Obando Ceron

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

pichedereck@gmail.com

Site web

Esra'a Saleh

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Nithya Shikarpur

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Anna (Cheng-Zhi) Huang

Shawn Tan

Doctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

(Rex) Devon Hjelm

Ankit Vani

Doctorat - UdeM

Site web

Yusong Wu

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Anna (Cheng-Zhi) Huang

Dinghuai Zhang

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Hattie Zhou

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Hugo Larochelle

Publications

Online black-box adaptation to label-shift in the presence of conditional-shift

Faruk Ahmed

We consider an out-of-distribution setting where trained predictive models are deployed online in new locations (inducing conditional-shift)… (voir plus), such that these locations are also associated with differently skewed target distributions (label-shift). While approaches for online adaptation to label-shift have recently been discussed by Wu et al. (2021), the potential presence of concurrent conditional-shift has not been considered in the literature, although one might anticipate such distributional shifts in realistic deployments. In this paper, we empirically explore the effectiveness of online adaptation methods in such situations on three synthetic and two realistic datasets, comprising both classification and regression problems. We show that it is possible to improve performance in these settings by learning additional hyper-parameters to account for the presence of conditional-shift by using appropriate validation sets.

2023-02-01

ICLR.cc/2023/Conference (rejected)

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier

Pierluca D'Oro

Max Schwarzer

Evgenii Nikishin

Pierre-Luc Bacon

Increasing the replay ratio, the number of updates of an agent's parameters per environment interaction, is an appealing strategy for improv… (voir plus)ing the sample efficiency of deep reinforcement learning algorithms. In this work, we show that fully or partially resetting the parameters of deep reinforcement learning agents causes better replay ratio scaling capabilities to emerge. We push the limits of the sample efficiency of carefully-modified algorithms by training them using an order of magnitude more updates than usual, significantly improving their performance in the Atari 100k and DeepMind Control Suite benchmarks. We then provide an analysis of the design choices required for favorable replay ratio scaling to be possible and discuss inherent limits and tradeoffs.

2023-02-01

ICLR.cc/2023/Conference (notable)

Simplicial Embeddings in Self-Supervised Learning and Downstream Classification

Samuel Lavoie

Christos Tsirigotis

Max Schwarzer

Kenji Kawaguchi

Ankit Vani

Michael Noukhovitch

Simplicial Embeddings (SEM) are representations learned through self-supervised learning (SSL), wherein a representation is projected into …

2023-02-01

ICLR.cc/2023/Conference (notable)

Bigger, Better, Faster: Human-level Atari with human-level efficiency

Max Schwarzer

Johan Samir Obando Ceron

Rishabh Agarwal

Pablo Samuel Castro

We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on sca… (voir plus)ling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide insights for future work. We end with a discussion about updating the goalposts for sample-efficient RL research on the ALE. We make our code and data publicly available at https://github.com/google-research/google-research/tree/master/bigger_better_faster.

2023-01-01

ICML (publié)

Expressiveness and Learnability: A Unifying View for Evaluating Self-Supervised Learning

Yuchen Lu

Zhen Liu

Aristide Baratin

Romain Laroche

Alessandro Sordoni

2023-01-01

Trans. Mach. Learn. Res. (publié)

Noisy Pairing and Partial Supervision for Stylized Opinion Summarization

Reinald Kim

Mirella Lapata. 2020

Un-611

David Scott Krueger

Emmanuel Bengio

Maxinder S. Kan-620

Tegan Maharaj

Asja Fischer

Somnath Basu

Roy Chowdhury

Chao Zhao

Tanya Goyal

Junyi Jiacheng Xu

Jessy Li

Ivor Wai-hung Tsang

James T. Kwok

Neil Houlsby

Andrei Giurgiu

Stanisław Jastrzębski … (voir 22 de plus)

Bruna Morrone

Quentin de Laroussilhe

Mona Gesmundo

Attariyan Sylvain

Gelly

Thomas Wolf

Lysandre Debut

Julien Victor Sanh

Clement Chaumond

Anthony Delangue

Pier-339 Moi

Tim ric Cistac

R´emi Rault

Morgan Louf

Funtow-900 Joe

Sam Davison

Patrick Shleifer

Von Platen

Clara Ma

Yacine Jernite

Julien Plu

Canwen Xu

Opinion summarization research has primar-001 ily focused on generating summaries reflect-002 ing important opinions from customer reviews 0… (voir plus)03 without paying much attention to the writing 004 style. In this paper, we propose the stylized 005 opinion summarization task, which aims to 006 generate a summary of customer reviews in 007 the desired (e.g., professional) writing style. 008 To tackle the difficulty in collecting customer 009 and professional review pairs, we develop a 010 non-parallel training framework, Noisy Pair-011 ing and Partial Supervision ( NAPA ), which 012 trains a stylized opinion summarization sys-013 tem from non-parallel customer and profes-014 sional review sets. We create a benchmark P RO - 015 S UM by collecting customer and professional 016 reviews from Yelp and Michelin. Experimental 017 results on P RO S UM and FewSum demonstrate 018 that our non-parallel training framework con-019 sistently improves both automatic and human 020 evaluations, successfully building a stylized 021 opinion summarization model that can gener-022 ate professionally-written summaries from cus-023 tomer reviews. 024

2023-01-01

(publié)

www.semanticscholar.org

Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods

Yuchen Lu

Zhen Liu

Aristide Baratin

Romain Laroche

Alessandro Sordoni

2023-01-01

Trans. Mach. Learn. Res. (publié)

Versatile Energy-Based Models for High Energy Physics

Taoli Cheng

2023-01-01

arXiv.org (prépublication)

Teaching Algorithmic Reasoning via In-context Learning

Hattie Zhou

Azade Nova

Hugo Larochelle

Behnam Neyshabur

Hanie Sedghi

2022-11-15

ArXiv (preprint)

Invariant representation driven neural classifier for anti-QCD jet tagging

Taoli Cheng

2022-10-24

Journal of High Energy Physics (publié)

arxiv.org

VIM: Variational Independent Modules for Video Prediction

Rim Assouel

Lluis Castrejon

Nicolas Ballas

Yoshua Bengio

We introduce a variational inference model called VIM, for Variational Independent Modules, for sequential data that learns and infers laten… (voir plus)t representations as a set of objects and discovers modular causal mechanisms over these objects. These mechanisms - which we call modules - are independently parametrized, define the stochastic transitions of entities and are shared across entities. At each time step, our model infers from a low-level input sequence a high-level sequence of categorical latent variables to select which transition modules to apply to which high-level object. We evaluate this model in video prediction tasks where the goal is to predict multi-modal future events given previous observations. We demonstrate empirically that VIM can model 2D visual sequences in an interpretable way and is able to identify the underlying dynamically instantiated mechanisms of the generation process. We additionally show that the learnt modules can be composed at test time to generalize to out-of-distribution observations.

2022-06-28

Proceedings of the First Conference on Causal Learning and Reasoning (publié)

proceedings.mlr.press

Multi-label Iterated Learning for Image Classification with Label Ambiguity

Sai Rajeswar

Pau Rodriguez

Soumye Singhal

David Vazquez

Transfer learning from large-scale pre-trained models has become essential for many computer vision tasks. Recent studies have shown that da… (voir plus)tasets like ImageNet are weakly labeled since images with multiple object classes present are assigned a single label. This ambiguity biases models towards a single prediction, which could result in the suppression of classes that tend to co-occur in the data. Inspired by language emergence literature, we propose multi-label iterated learning (MILe) to incorporate the inductive biases of multi-label learning from single labels using the framework of iterated learning. MILe is a simple yet effective procedure that builds a multi-label description of the image by propagating binary predictions through successive generations of teacher and student networks with a learning bottleneck. Experiments show that our approach exhibits systematic benefits on ImageNet accuracy as well as ReaL F1 score, which indicates that MILe deals better with label ambiguity than the standard training procedure, even when fine-tuning from self-supervised weights. We also show that MILe is effective reducing label noise, achieving state-of-the-art performance on real-world large-scale noisy data such as WebVision. Furthermore, MILe improves performance in class incremental settings such as IIRC and it is robust to distribution shifts. Code: https://github.com/rajeswar18/MILe

2022-06-18

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)