Deleu, Tristan

20 publications

NeurIPS 2025 Gymnasium: A Standard Interface for Reinforcement Learning Environments Mark Towers, Ariel Kwiatkowski, John U. Balis, Gianluca De Cola, Tristan Deleu, Manuel Goulão, Kallinteris Andreas, Markus Krimmel, Arjun Kg, Rodrigo De Lazcano Perez-Vicente, J K Terry, Andrea Pierré, Sander V Schulhoff, Jun Jet Tai, Hannah Tan, Omar G. Younis

UAI 2024 Discrete Probabilistic Inference as Control in Multi-Path Environments Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio

ICML 2023 A Theory of Continuous Generative Flow Networks Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-Garcı́a, Lena Nehale Ezzine, Yoshua Bengio, Nikolay Malkin

ICMLW 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

ICMLW 2023 Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu, Stefan Bauer, Yoshua Bengio

JMLR 2023 GFlowNet Foundations Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio

ICLR 2023 GFlowNets and Variational Inference Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J Hu, Katie E Everett, Dinghuai Zhang, Yoshua Bengio

NeurIPS 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

ICMLW 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

ICML 2023 Synergies Between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning Sebastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand

AAAI 2023 The Effect of Diversity in Meta-Learning Ramnath Kumar, Tristan Deleu, Yoshua Bengio

UAI 2022 Bayesian Structure Learning with Generative Flow Networks Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio

ICLRW 2022 Bayesian Structure Learning with Generative Flow Networks Tristan Deleu, António Góis, Chris Chinenye Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio

ICLR 2022 Continuous-Time Meta-Learning with Forward Mode Differentiation Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon

NeurIPSW 2022 Rethinking Learning Dynamics in RL Using Adversarial Networks Ramnath Kumar, Tristan Deleu, Yoshua Bengio

NeurIPSW 2021 Effect of Diversity in Meta-Learning Ramnath Kumar, Tristan Deleu, Yoshua Bengio

ICLR 2021 Predicting Infectiousness for Proactive Contact Tracing Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Benjamin Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilaniuk, David Buckeridge, Gaetan Caron, Pierre Luc Carrier, Joumana Ghosn, Satya Ortiz Gagne, Christopher Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

ICLR 2020 A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher Pal

ICLR 2020 Gradient-Based Neural DAG Learning Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien

NeurIPSW 2019 Gradient-Based Neural DAG Learning Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien