Search Results for author: Tristan Deleu

Found 28 papers, 15 papers with code

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

no code implementations11 Jul 2023 Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu, Stefan Bauer, Yoshua Bengio

Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference.

Benchmarking Causal Discovery +2

Generative Flow Networks: a Markov Chain Perspective

no code implementations4 Jul 2023 Tristan Deleu, Yoshua Bengio

While Markov chain Monte Carlo methods (MCMC) provide a general framework to sample from a probability distribution defined up to normalization, they often suffer from slow convergence to the target distribution when the latter is highly multi-modal.

Decision Making

BatchGFN: Generative Flow Networks for Batch Active Learning

1 code implementation26 Jun 2023 Shreshth A. Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal

We introduce BatchGFN -- a novel approach for pool-based active learning that uses generative flow networks to sample sets of data points proportional to a batch reward.

Active Learning

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network

no code implementations30 May 2023 Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin, Laurent Charlin, Yoshua Bengio

Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied to the problem of inferring the marginal posterior distribution over the directed acyclic graph (DAG) of a Bayesian Network, given a dataset of observations.

Bayesian Inference

GFlowNets for AI-Driven Scientific Discovery

no code implementations1 Feb 2023 Moksh Jain, Tristan Deleu, Jason Hartford, Cheng-Hao Liu, Alex Hernandez-Garcia, Yoshua Bengio

However, in order to truly leverage large-scale data sets and high-throughput experimental setups, machine learning methods will need to be further improved and better integrated in the scientific discovery pipeline.

Efficient Exploration Experimental Design

A theory of continuous generative flow networks

1 code implementation30 Jan 2023 Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin

Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects.

Variational Inference

Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes

no code implementations4 Nov 2022 Mizu Nishikawa-Toomey, Tristan Deleu, Jithendaraa Subramanian, Yoshua Bengio, Laurent Charlin

We extend the method of Bayesian causal structure learning using GFlowNets to learn not only the posterior distribution over the structure, but also the parameters of a linear-Gaussian model.

Learning Latent Structural Causal Models

no code implementations24 Oct 2022 Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke, Tristan Deleu, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou

For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions.

Bayesian Inference Image Generation +1

GFlowNets and variational inference

1 code implementation2 Oct 2022 Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio

This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs.

Reinforcement Learning (RL) Variational Inference

Continuous-Time Meta-Learning with Forward Mode Differentiation

no code implementations ICLR 2022 Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon

Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm where adaptation follows the dynamics of a gradient vector field.

Few-Shot Image Classification Meta-Learning

Bayesian Structure Learning with Generative Flow Networks

1 code implementation28 Feb 2022 Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio

In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian networks, from data.

Variational Inference

Boosting Exploration in Multi-Task Reinforcement Learning using Adversarial Networks

1 code implementation27 Jan 2022 Ramnath Kumar, Tristan Deleu, Yoshua Bengio

Our proposed adversarial training regime for Multi-Task Reinforcement Learning (MT-RL) addresses the limitations of conventional training methods in RL, especially in meta-RL environments where the agent faces new tasks.

Decision Making reinforcement-learning +1

The Effect of Diversity in Meta-Learning

1 code implementation27 Jan 2022 Ramnath Kumar, Tristan Deleu, Yoshua Bengio

Recent studies show that task distribution plays a vital role in the meta-learner's performance.

Few-Shot Learning

GFlowNet Foundations

2 code implementations17 Nov 2021 Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio

Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them approximately sample in proportion to a given reward function.

Active Learning

Structured Sparsity Inducing Adaptive Optimizers for Deep Learning

1 code implementation7 Feb 2021 Tristan Deleu, Yoshua Bengio

The parameters of a neural network are naturally organized in groups, some of which might not contribute to its overall performance.

Predicting Infectiousness for Proactive Contact Tracing

1 code implementation ICLR 2021 Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT).

Curriculum in Gradient-Based Meta-Reinforcement Learning

no code implementations19 Feb 2020 Bhairav Mehta, Tristan Deleu, Sharath Chandra Raparthy, Chris J. Pal, Liam Paull

However, specifically in the case of meta-reinforcement learning (meta-RL), we can show that gradient-based meta-learners are sensitive to task distributions.

Benchmarking Meta-Learning +4

The TCGA Meta-Dataset Clinical Benchmark

1 code implementation18 Oct 2019 Mandana Samiei, Tobias Würfl, Tristan Deleu, Martin Weiss, Francis Dutil, Thomas Fevens, Geneviève Boucher, Sebastien Lemieux, Joseph Paul Cohen

Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals.

Decision Making

Torchmeta: A Meta-Learning library for PyTorch

5 code implementations14 Sep 2019 Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, Yoshua Bengio

The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research.

Meta-Learning

Learning Powerful Policies by Using Consistent Dynamics Model

1 code implementation11 Jun 2019 Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Sergey Levine, Jian Tang

There is enough evidence that humans build a model of the environment, not only by observing the environment but also by interacting with the environment.

Atari Games Model-based Reinforcement Learning +1

Gradient-Based Neural DAG Learning

1 code implementation ICLR 2020 Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien

We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data.

Causal Inference

The effects of negative adaptation in Model-Agnostic Meta-Learning

no code implementations5 Dec 2018 Tristan Deleu, Yoshua Bengio

The capacity of meta-learning algorithms to quickly adapt to a variety of tasks, including ones they did not experience during meta-training, has been a key factor in the recent success of these methods on few-shot learning problems.

Few-Shot Learning Meta Reinforcement Learning +2

Learning powerful policies and better dynamics models by encouraging consistency

no code implementations27 Sep 2018 Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Jian Tang

Analogously, we would expect such interaction to be helpful for a learning agent while learning to model the environment dynamics.

Model-based Reinforcement Learning

Learning Operations on a Stack with Neural Turing Machines

no code implementations2 Dec 2016 Tristan Deleu, Joseph Dureau

Multiple extensions of Recurrent Neural Networks (RNNs) have been proposed recently to address the difficulty of storing information over long time periods.

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