Learning Operations on a Stack with Neural Turing Machines. - Tristan Deleu; Joseph Dureau (CoRR, 2016)

The effects of negative adaptation in Model-Agnostic Meta-Learning. - Tristan Deleu; Yoshua Bengio (CoRR, 2018)

Learning Powerful Policies by Using Consistent Dynamics Model. - Shagun Sodhani; Anirudh Goyal; Tristan Deleu; Yoshua Bengio; Sergey Levine; Jian Tang 005 (CoRR, 2019)

Torchmeta: A Meta-Learning library for PyTorch. - Tristan Deleu; Tobias Würfl; Mandana Samiei; Joseph Paul Cohen; Yoshua Bengio (CoRR, 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)

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 (ICLR, 2020)

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

Curriculum in Gradient-Based Meta-Reinforcement Learning. - Bhairav Mehta; Tristan Deleu; Sharath Chandra Raparthy; Christopher J. Pal; Liam Paull (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; et al. (CoRR, 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; et al. (CoRR, 2020)

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; et al. (ICLR, 2021)

Structured Sparsity Inducing Adaptive Optimizers for Deep Learning. - Tristan Deleu; Yoshua Bengio (CoRR, 2021)

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

Bayesian structure learning with generative flow networks. - Tristan Deleu; António Góis; Chris Emezue; Mansi Rankawat; Simon Lacoste-Julien; Stefan Bauer; Yoshua Bengio (UAI, 2022)

Rethinking Learning Dynamics in RL using Adversarial Networks. - Ramnath Kumar; Tristan Deleu; Yoshua Bengio (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)

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)

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)

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

GFlowNets and variational inference. - Nikolay Malkin; Salem Lahlou; Tristan Deleu; Xu Ji; Edward J. Hu; Katie Everett; Dinghuai Zhang; Yoshua Bengio (ICLR, 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 (ICML, 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 (ICML, 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 (NeurIPS, 2023)

GFlowNets for AI-Driven Scientific Discovery. - Moksh Jain; Tristan Deleu; Jason S. Hartford; Cheng-Hao Liu; Alex Hernández-García; 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)

Generative Flow Networks: a Markov Chain Perspective. - Tristan Deleu; Yoshua Bengio (CoRR, 2023)

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation. - Chris Chinenye Emezue; Alexandre Drouin; Tristan Deleu; Stefan Bauer; Yoshua Bengio (CoRR, 2023)

GFlowNet Foundations. - Yoshua Bengio; Salem Lahlou; Tristan Deleu; Edward J. Hu; Mo Tiwari; Emmanuel Bengio (J. Mach. Learn. Res., 2023)

Discrete Probabilistic Inference as Control in Multi-path Environments. - Tristan Deleu; Padideh Nouri; Nikolay Malkin; Doina Precup; Yoshua Bengio (UAI, 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; et al. (CoRR, 2024)

Generative Flow Networks: Theory and Applications to Structure Learning. - Tristan Deleu (CoRR, 2025)