Tristan Deleu - RankMe

43 papers A* 9A 2Journal 32

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Generative Flow Networks: Theory and Applications to Structure Learning. CoRR Tristan Deleu
2025	J	`jnl`	Relative Trajectory Balance is equivalent to Trust-PCL. CoRR Tristan Deleu, Padideh Nouri, Yoshua Bengio, Doina Precup
2024	A	`conf`	Discrete Probabilistic Inference as Control in Multi-path Environments. UAI Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio
2024	J	`jnl`	Discrete Probabilistic Inference as Control in Multi-path Environments. CoRR Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio
2024	J	`jnl`	Gymnasium: A Standard Interface for Reinforcement Learning Environments. CoRR 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
2023	A*	`conf`	A theory of continuous generative flow networks. ICML Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin
2023	J	`jnl`	A theory of continuous generative flow networks. CoRR Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin
2023	J	`jnl`	BatchGFN: Generative Flow Networks for Batch Active Learning. CoRR Shreshth A. Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal
2023	J	`jnl`	Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation. CoRR Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu, Stefan Bauer, Yoshua Bengio
2023	J	`jnl`	GFlowNet Foundations. J. Mach. Learn. Res. Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio
2023	A*	`conf`	GFlowNets and variational inference. ICLR Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio
2023	J	`jnl`	GFlowNets for AI-Driven Scientific Discovery. CoRR Moksh Jain, Tristan Deleu, Jason S. Hartford, Cheng-Hao Liu, Alex Hernández-García, Yoshua Bengio
2023	J	`jnl`	Generative Flow Networks: a Markov Chain Perspective. CoRR Tristan Deleu, Yoshua Bengio
2023	A*	`conf`	Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network. NeurIPS Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin, Laurent Charlin, Yoshua Bengio
2023	J	`jnl`	Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network. CoRR Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin, Laurent Charlin, Yoshua Bengio
2023	A*	`conf`	Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning. ICML Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand
2023	A*	`conf`	The Effect of Diversity in Meta-Learning. AAAI Ramnath Kumar, Tristan Deleu, Yoshua Bengio
2022	J	`jnl`	Bayesian Structure Learning with Generative Flow Networks. CoRR Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio
2022	J	`jnl`	Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes. CoRR Mizu Nishikawa-Toomey, Tristan Deleu, Jithendaraa Subramanian, Yoshua Bengio, Laurent Charlin
2022	A	`conf`	Bayesian structure learning with generative flow networks. UAI Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio
2022	A*	`conf`	Continuous-Time Meta-Learning with Forward Mode Differentiation. ICLR Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon
2022	J	`jnl`	Continuous-Time Meta-Learning with Forward Mode Differentiation. CoRR Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon
2022	J	`jnl`	GFlowNets and variational inference. CoRR Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio
2022	J	`jnl`	Learning Latent Structural Causal Models. CoRR Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke, Tristan Deleu, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou
2022	J	`jnl`	Rethinking Learning Dynamics in RL using Adversarial Networks. CoRR Ramnath Kumar, Tristan Deleu, Yoshua Bengio
2022	J	`jnl`	Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective. CoRR Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand
2022	J	`jnl`	The Effect of Diversity in Meta-Learning. CoRR Ramnath Kumar, Tristan Deleu, Yoshua Bengio
2021	J	`jnl`	GFlowNet Foundations. CoRR Yoshua Bengio, Tristan Deleu, Edward J. Hu, Salem Lahlou, Mo Tiwari, Emmanuel Bengio
2021	A*	`conf`	Predicting Infectiousness for Proactive Contact Tracing. ICLR 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
2021	J	`jnl`	Structured Sparsity Inducing Adaptive Optimizers for Deep Learning. CoRR Tristan Deleu, Yoshua Bengio
2020	A*	`conf`	A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. ICLR Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher J. Pal
2020	J	`jnl`	COVI White Paper. CoRR 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
2020	J	`jnl`	COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing. CoRR 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
2020	J	`jnl`	Curriculum in Gradient-Based Meta-Reinforcement Learning. CoRR Bhairav Mehta, Tristan Deleu, Sharath Chandra Raparthy, Christopher J. Pal, Liam Paull
2020	A*	`conf`	Gradient-Based Neural DAG Learning. ICLR Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien
2020	J	`jnl`	Predicting Infectiousness for Proactive Contact Tracing. CoRR Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif B. 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, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams
2019	J	`jnl`	A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. CoRR Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher J. Pal
2019	J	`jnl`	Gradient-Based Neural DAG Learning. CoRR Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien
2019	J	`jnl`	Learning Powerful Policies by Using Consistent Dynamics Model. CoRR Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Sergey Levine, Jian Tang
2019	J	`jnl`	The TCGA Meta-Dataset Clinical Benchmark. CoRR Mandana Samiei, Tobias Würfl, Tristan Deleu, Martin Weiss, Francis Dutil, Thomas Fevens, Geneviève Boucher, Sébastien Lemieux, Joseph Paul Cohen
2019	J	`jnl`	Torchmeta: A Meta-Learning library for PyTorch. CoRR Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, Yoshua Bengio
2018	J	`jnl`	The effects of negative adaptation in Model-Agnostic Meta-Learning. CoRR Tristan Deleu, Yoshua Bengio
2016	J	`jnl`	Learning Operations on a Stack with Neural Turing Machines. CoRR Tristan Deleu, Joseph Dureau