Tristan Deleu

According to our database, Tristan Deleu authored at least 30 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Gymnasium: A Standard Interface for Reinforcement Learning Environments.

Mark Towers, Ariel Kwiatkowski, Jordan K. Terry, John U. Balis, Gianluca De Cola, Tristan Deleu, Manuel Goulão, Andreas Kallinteris, Markus Krimmel, Arjun KG, Rodrigo Perez-Vicente, Andrea Pierré, Sander Schulhoff, Jun Jet Tai, Hannah Tan, Omar G. Younis

CoRR, 2024

Discrete Probabilistic Inference as Control in Multi-path Environments.

Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio

CoRR, 2024

2023

GFlowNet Foundations.

Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio

J. Mach. Learn. Res., 2023

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation.

Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu, Stefan Bauer, Yoshua Bengio

CoRR, 2023

Generative Flow Networks: a Markov Chain Perspective.

Tristan Deleu, Yoshua Bengio

CoRR, 2023

BatchGFN: Generative Flow Networks for Batch Active Learning.

Shreshth A. Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal

CoRR, 2023

GFlowNets for AI-Driven Scientific Discovery.

Moksh Jain, Tristan Deleu, Jason S. Hartford, Cheng-Hao Liu, Alex Hernández-García, Yoshua Bengio

CoRR, 2023

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network.

Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin, Laurent Charlin, Yoshua Bengio

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A theory of continuous generative flow networks.

Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin

Proceedings of the International Conference on Machine Learning, 2023

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning.

Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand

Proceedings of the International Conference on Machine Learning, 2023

GFlowNets and variational inference.

Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio

Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Effect of Diversity in Meta-Learning.

Ramnath Kumar, Tristan Deleu, Yoshua Bengio

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective.

Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand

CoRR, 2022

Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes.

Mizu Nishikawa-Toomey, Tristan Deleu, Jithendaraa Subramanian, Yoshua Bengio, Laurent Charlin

CoRR, 2022

Learning Latent Structural Causal Models.

Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke, Tristan Deleu, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou

CoRR, 2022

Rethinking Learning Dynamics in RL using Adversarial Networks.

Ramnath Kumar, Tristan Deleu, Yoshua Bengio

CoRR, 2022

Bayesian structure learning with generative flow networks.

Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Continuous-Time Meta-Learning with Forward Mode Differentiation.

Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Structured Sparsity Inducing Adaptive Optimizers for Deep Learning.

Tristan Deleu, Yoshua Bengio

CoRR, 2021

Predicting Infectiousness for Proactive Contact Tracing.

Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Benjamin Müller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilaniuk, David L. Buckeridge, Gaétan Marceau-Caron, Pierre Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Christopher J. Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing.

Prateek Gupta, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, Soren Harnois-Leblanc, Victor Schmidt, Pierre-Luc St-Charles, Tristan Deleu, Andrew Williams, Akshay Patel, Meng Qu, Olexa Bilaniuk, Gaétan Marceau Caron, Pierre Luc Carrier, Satya Ortiz-Gagné, Marc-Andre Rousseau, David L. Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Joseph Pal, Joanna Merckx, Eilif B. Müller, Yoshua Bengio

CoRR, 2020

COVI White Paper.

Hannah Alsdurf, Yoshua Bengio, Tristan Deleu, Prateek Gupta, Daphne Ippolito, Richard Janda, Max Jarvie, Tyler Kolody, Sekoul Krastev, Tegan Maharaj, Robert Obryk, Dan Pilat, Valerie Pisano, Benjamin Prud'homme, Meng Qu, Nasim Rahaman, Irina Rish, Jean-Franois Rousseau, Abhinav Sharma, Brooke Struck, Jian Tang, Martin Weiss, Yun William Yu

CoRR, 2020

Curriculum in Gradient-Based Meta-Reinforcement Learning.

Bhairav Mehta, Tristan Deleu, Sharath Chandra Raparthy, Christopher J. Pal, Liam Paull

CoRR, 2020

Gradient-Based Neural DAG Learning.

Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien

Proceedings of the 8th International Conference on Learning Representations, 2020

A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms.

Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher J. Pal

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

The TCGA Meta-Dataset Clinical Benchmark.

Mandana Samiei, Tobias Würfl, Tristan Deleu, Martin Weiss, Francis Dutil, Thomas Fevens, Geneviève Boucher, Sébastien Lemieux, Joseph Paul Cohen

CoRR, 2019

Torchmeta: A Meta-Learning library for PyTorch.

Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, Yoshua Bengio

CoRR, 2019

Learning Powerful Policies by Using Consistent Dynamics Model.

Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Sergey Levine, Jian Tang

CoRR, 2019

2018

The effects of negative adaptation in Model-Agnostic Meta-Learning.

Tristan Deleu, Yoshua Bengio

CoRR, 2018

2016

Learning Operations on a Stack with Neural Turing Machines.

Tristan Deleu, Joseph Dureau

CoRR, 2016