Aaron Courville

alexandre.ganito@mila.quebec

adrien.taiga@mila.quebec

Alexandre Diz Ganito

Maîtrise recherche - Université de Montréal

Amr Khalifa

Doctorat - Université de Montréal

amr-khalifa.alshaykh@mila.quebec

andrei.nicolicioiu@mila.quebec

Andrei Nicolicioiu

Doctorat - Université de Montréal

ching-lam.choi@mila.quebec

Ankit Vani

Doctorat - Université de Montréal

Baccalauréat - Université de Montréal

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Doctorat - Université de Montréal

Co-superviseur⋅e :

Yoshua Bengio

dinghuai.zhang@mila.quebec

Esra'a Saleh

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Glen Berseth

esraa.saleh@mila.quebec

evgenii.nikishin@mila.quebec

Evgenii Nikishin

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Pierre-Luc Bacon

Faruk Ahmed

Doctorat - Université de Montréal

faruk.ahmed@mila.quebec

Johan Samir Obando Ceron

Doctorat - Université de Montréal

Co-superviseur⋅e :

Pablo Samuel Castro

johan.ceron@mila.quebec

Juan Duque

Doctorat - Université de Montréal

juan.duque@mila.quebec

manoosh.samiei@mila.quebec

Manoosh Samiei

Doctorat - Université de Montréal

Doctorat - Université de Montréal

Co-superviseur⋅e :

mohammed.muqeeth@mila.quebec

schwarzm@mila.quebec

Hattie Zhou

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Hugo Larochelle

mengfei.zhou@mila.quebec

Michael Noukhovitch

Doctorat - Université de Montréal

Collaborateur·rice de recherche

morgane.moss@mila.quebec

Muqeeth Mohammed

Collaborateur·rice de recherche - Université de Montréal

Stagiaire de recherche - Ghent University

pietro.mazzaglia@mila.quebec

Stagiaire de recherche - Université de Montréal

razvan.ciuca@mila.quebec

Rishabh Agarwal

Doctorat - Université de Montréal

Co-superviseur⋅e :

samuel.lavoie@mila.quebec

Samuel Lavoie Marchildon

Doctorat - Université de Montréal

Sarvjeet Ghotra

Maîtrise recherche - Université de Montréal

Superviseur⋅e principal⋅e :

Aishwarya Agrawal

sarvjeet-singh.ghotra@mila.quebec

xiaofeng.zhang@mila.quebec

Arian Hosseini

Doctorat - Université de Montréal

Shawn Tan

Doctorat - Université de Montréal

Doctorat - Université de Montréal

Doctorat - Université de Montréal

Yusong Wu

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Anna (Cheng-Zhi) Huang

wu.yusong@mila.quebec

Zhixuan Lin

Doctorat - Université de Montréal

zhixuan.lin@mila.quebec

Publications

Sparse Universal Transformer

Shawn Tan

Yikang Shen

Zhenfang Chen

Chuang Gan

The Universal Transformer (UT) is a variant of the Transformer that shares parameters across its layers and is Turing-complete under certain… (voir plus) assumptions. Empirical evidence also shows that UTs have better compositional generalization than Vanilla Transformers (VTs) in formal language tasks. The parameter-sharing also affords it better parameter efficiency than VTs. Despite its many advantages, most state-of-the-art NLP systems use VTs as their backbone model instead of UTs. This is mainly because scaling UT parameters is more compute and memory intensive than scaling up a VT. This paper proposes the Sparse Universal Transformer (SUT), which leverages Sparse Mixture of Experts (SMoE) to reduce UT's computation complexity while retaining its parameter efficiency and generalization ability. Experiments show that SUT combines the best of both worlds, achieving strong generalization results on formal language tasks (Logical inference and CFQ) and impressive parameter and computation efficiency on standard natural language benchmarks like WMT'14.

2023-10-07

EMNLP/2023/Conference (accepté)

Double Gumbel Q-Learning.

David Yu-Tung Hui

Pierre-Luc Bacon

Group Robust Classification Without Any Group Information

Christos Tsirigotis

Joao Monteiro

Pau Rodriguez

David Vazquez

Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings

Yi Ren

Samuel Lavoie

Mikhail Galkin

Danica J. Sutherland

Language Model Alignment with Elastic Reset

Michael Noukhovitch

Samuel Lavoie

Florian Strub

Finetuning language models with reinforcement learning (RL), e.g. from human feedback (HF), is a prominent method for alignment. But optimiz… (voir plus)ing against a reward model can improve on reward while degrading performance in other areas, a phenomenon known as reward hacking, alignment tax, or language drift. First, we argue that commonly-used test metrics are insufficient and instead measure how different algorithms tradeoff between reward and drift. The standard method modified the reward with a Kullback-Lieber (KL) penalty between the online and initial model. We propose Elastic Reset, a new algorithm that achieves higher reward with less drift without explicitly modifying the training objective. We periodically reset the online model to an exponentially moving average (EMA) of itself, then reset the EMA model to the initial model. Through the use of an EMA, our model recovers quickly after resets and achieves higher reward with less drift in the same number of steps. We demonstrate that fine-tuning language models with Elastic Reset leads to state-of-the-art performance on a small scale pivot-translation benchmark, outperforms all baselines in a medium-scale RLHF-like IMDB mock sentiment task and leads to a more performant and more aligned technical QA chatbot with LLaMA-7B. Code available at github.com/mnoukhov/elastic-reset.

Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets

Dinghuai Zhang

Hanjun Dai

Nikolay Malkin

Yoshua Bengio

Ling Pan

Versatile Energy-Based Probabilistic Models for High Energy Physics

Taoli Cheng

Discovering the Electron Beam Induced Transition Rates for Silicon Dopants in Graphene with Deep Neural Networks in the STEM

Kevin M Roccapriore

Max Schwarzer

Joshua Greaves

Jesse Farebrother

Rishabh Agarwal

Colton Bishop

Maxim Ziatdinov

Igor Mordatch

Ekin Dogus Cubuk

Pablo Samuel Castro

Sergei V Kalinin

2023-07-22

Microscopy and Microanalysis (publié)

Meta-Value Learning: a General Framework for Learning with Learning Awareness

Tim Cooijmans

Milad Aghajohari

2023-07-17

ArXiv (preprint)

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.

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (publié)

Learning with Learning Awareness using Meta-Values

Tim Cooijmans

Milad Aghajohari

2023-06-19

ICML.cc/2023/Workshop/Frontiers4LCD (publié)

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

Dinghuai Zhang

Hanjun Dai

Nikolay Malkin

Yoshua Bengio

Ling Pan

Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact algorithms, making them a tempting domain to appl… (voir plus)y machine learning methods. The highly structured constraints in these problems can hinder either optimization or sampling directly in the solution space. On the other hand, GFlowNets have recently emerged as a powerful machinery to efficiently sample from composite unnormalized densities sequentially and have the potential to amortize such solution-searching processes in CO, as well as generate diverse solution candidates. In this paper, we design Markov decision processes (MDPs) for different combinatorial problems and propose to train conditional GFlowNets to sample from the solution space. Efficient training techniques are also developed to benefit long-range credit assignment. Through extensive experiments on a variety of different CO tasks with synthetic and realistic data, we demonstrate that GFlowNet policies can efficiently find high-quality solutions. Our implementation is open-sourced at https://github.com/zdhNarsil/GFlowNet-CombOpt.

2023-05-26

ArXiv (prépublication)