publications

2024

  1. UAI
    Discrete Probabilistic Inference as Control in Multi-path Environments
    Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, and Yoshua Bengio
    Uncertainty in Artificial Intelligence, 2024
    arXiv

2023

  1. NeurIPS
    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, and Yoshua Bengio
    Conference on Neural Information Processing Systems, 2023
    arXiv
  2. ICML
    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, and Quentin Bertrand
    International Conference on Machine Learning, 2023
    arXiv
  3. ICML
    A Theory of Continuous Generative Flow Networks
    Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernandez-Garcia, Lena Nehale Ezzine, Yoshua Bengio, and Nikolay Malkin
    International Conference on Machine Learning, 2023
    arXiv
  4. ICLR
    GFlowNets and variational inference
    Nikolay Malkin*, Salem Lahlou*, Tristan Deleu*, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, and Yoshua Bengio
    International Conference on Learning Representations, 2023
    arXiv
  5. Journal
    GFlowNets for AI-Driven Scientific Discovery
    Moksh Jain, Tristan Deleu, Jason Hartford, Cheng-Hao Liu, Alex Hernandez-Garcia, and Yoshua Bengio
    Digital Discovery, Royal Society of Chemistry, 2023
    arXiv
  6. AAAI Oral
    The Effect of Diversity in Meta-Learning
    Ramnath Kumar, Tristan Deleu, and Yoshua Bengio
    AAAI Conference on Artificial Intelligence, 2023
    arXiv

2022

  1. UAI
    Bayesian Structure Learning with Generative Flow Networks
    Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, and Yoshua Bengio
    Uncertainty in Artificial Intelligence, 2022
    arXiv Code
  2. ICLR Spotlight
    Continuous-Time Meta-Learning with Forward Mode Differentiation
    Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, and Pierre-Luc Bacon
    International Conference on Learning Representations, 2022
    arXiv Code

2021

  1. JMLR
    GFlowNet Foundations
    Yoshua Bengio*, Salem Lahlou*, Tristan Deleu*, Edward J Hu, Mo Tiwari, and Emmanuel Bengio
    Journal of Machine Learning Research, 2021
    arXiv
  2. ICLR Spotlight
    Predicting Infectiousness for Proactive Contact Tracing
    Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, and  others
    International Conference on Learning Representations, 2021
    arXiv

2020

  1. ICLR
    A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
    Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, and Christopher Pal
    International Conference on Learning Representations, 2020
    arXiv
  2. ICLR
    Gradient-Based Neural DAG Learning
    Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien
    International Conference on Learning Representations, 2020
    arXiv

2019

  1. Preprint Best in Show
    Torchmeta: A Meta-Learning library for PyTorch
    Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, and Yoshua Bengio
    arXiv preprint, 2019
    arXiv Code