publications

2022

  1. Preprint
    Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes
    Mizu Nishikawa-Toomey*, Tristan Deleu*, Jithendaraa Subramanian, Yoshua Bengio, and Laurent Charlin
    arXiv preprint 2022
  2. Preprint
    Learning Latent Structural Causal Models
    arXiv preprint 2022
  3. Preprint
    GFlowNets and variational inference
    Nikolay Malkin*, Salem Lahlou*, Tristan Deleu*, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, and Yoshua Bengio
    arXiv preprint 2022
  4. UAI
    Bayesian Structure Learning with Generative Flow Networks
    Uncertainty in Artificial Intelligence 2022
  5. ICLR Spotlight
    Continuous-Time Meta-Learning with Forward Mode Differentiation
    International Conference on Learning Representations 2022
  6. Workshop
    Rethinking Learning Dynamics in RL using Adversarial Networks
    Ramnath Kumar, Tristan Deleu, and Yoshua Bengio
    Deep Reinforcement Learning Workshop, NeurIPS 2022

2021

  1. Workshop
    The Effect of Diversity in Meta-Learning
    Ramnath Kumar, Tristan Deleu, and Yoshua Bengio
    Workshop on Meta-Learning, NeurIPS 2021
  2. Preprint
    GFlowNet Foundations
    Yoshua Bengio*, Salem Lahlou*, Tristan Deleu*, Edward J Hu, Mo Tiwari, and Emmanuel Bengio
    arXiv preprint 2021
  3. Preprint
    Structured Sparsity Inducing Adaptive Optimizers for Deep Learning
    Tristan Deleu, and Yoshua Bengio
    arXiv preprint 2021
  4. 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

2020

  1. Preprint
    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, and others
    arXiv preprint 2020
  2. Preprint
    COVI White Paper
    Hannah Alsdurf, Yoshua Bengio, Tristan Deleu, Prateek Gupta, Daphne Ippolito, Richard Janda, Max Jarvie, Tyler Kolody, Sekoul Krastev, Tegan Maharaj, and others
    arXiv preprint 2020
  3. ICLR
    A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
    International Conference on Learning Representations 2020
  4. ICLR
    Gradient-Based Neural DAG Learning
    International Conference on Learning Representations 2020
  5. Preprint
    Curriculum in Gradient-Based Meta-Reinforcement Learning
    Bhairav Mehta, Tristan Deleu, Sharath Chandra Raparthy, Chris J Pal, and Liam Paull
    arXiv preprint 2020

2019

  1. Preprint
    The TCGA Meta-Dataset Clinical Bench-mark
    Mandana Samiei, Tobias Würfl, Tristan Deleu, Martin Weiss, Francis Dutil, Thomas Fevens, Geneviève Boucher, Sebastien Lemieux, and Joseph Paul Cohen
    arXiv preprint 2019
  2. Preprint Best in Show
    Torchmeta: A Meta-Learning library for PyTorch
    arXiv preprint 2019
  3. RLDM
    Learning powerful policies by using consistent dynamics model
    Reinforcement Learning and Decision Making 2019

2018

  1. Workshop
    The effects of negative adaptation in Model-Agnostic Meta-Learning
    Tristan Deleu, and Yoshua Bengio
    Workshop on Meta-Learning, NeurIPS 2018
  2. Workshop
    On the reproducibility of gradient-based Meta-Reinforcement Learning baselines
    Tristan Deleu, Simon Guiroy, and Seyedarian Hosseini
    Reproducibility in Machine Learning Workshop, ICML 2018
  3. Workshop
    Orion: Experiment Version Control for Efficient Hyperparameter Optimization
    Christos Tsirigotis, Xavier Bouthillier, François Corneau-Tremblay, Peter Henderson, Reyhane Askari, Samuel Lavoie-Marchildon, Tristan Deleu, Dendi Suhubdy, Michael Noukhovitch, Frédéric Bastien, and others
    Reproducibility in Machine Learning Workshop, ICML 2018

2016

  1. Workshop
    Learning Operations on a Stack with Neural Turing Machines
    Tristan Deleu, and Joseph Dureau
    1st Workshop on Neural Abstract Machines & Program Induction (NAMPI), NIPS 2016