Tristan Deleu | DeepAI

AI Chat

Featured Co-authors

Yoshua Bengio
448 publications
Sergey Levine
379 publications
Jian Tang
129 publications
Yarin Gal
126 publications
Simon Lacoste-Julien
79 publications
Stefan Bauer
68 publications
Christopher Pal
63 publications
Anirudh Goyal
61 publications
Ioannis Mitliagkas
46 publications
Laurent Charlin
43 publications
Joseph Paul Cohen
39 publications

∙ 07/11/2023

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

The practical utility of causality in decision-making is widespread and ...

0 Chris Chinenye Emezue, et al. ∙

∙ 07/04/2023

Generative Flow Networks: a Markov Chain Perspective

While Markov chain Monte Carlo methods (MCMC) provide a general framewor...

0 Tristan Deleu, et al. ∙

∙ 06/26/2023

BatchGFN: Generative Flow Networks for Batch Active Learning

We introduce BatchGFN – a novel approach for pool-based active learning ...

0 Shreshth A. Malik, et al. ∙

∙ 05/30/2023

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

Generative Flow Networks (GFlowNets), a class of generative models over ...

0 Tristan Deleu, et al. ∙

∙ 02/01/2023

GFlowNets for AI-Driven Scientific Discovery

Tackling the most pressing problems for humanity, such as the climate cr...

0 Moksh Jain, et al. ∙

∙ 01/30/2023

A theory of continuous generative flow networks

Generative flow networks (GFlowNets) are amortized variational inference...

0 Salem Lahlou, et al. ∙

∙ 11/26/2022

Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective

Although disentangled representations are often said to be beneficial fo...

0 Sébastien Lachapelle, et al. ∙

∙ 11/04/2022

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

Bayesian causal structure learning aims to learn a posterior distributio...

0 Mizu Nishikawa-Toomey, et al. ∙

∙ 10/24/2022

Learning Latent Structural Causal Models

Causal learning has long concerned itself with the accurate recovery of ...

0 Jithendaraa Subramanian, et al. ∙

∙ 10/02/2022

GFlowNets and variational inference

This paper builds bridges between two families of probabilistic algorith...

0 Nikolay Malkin, et al. ∙

∙ 03/02/2022

Continuous-Time Meta-Learning with Forward Mode Differentiation

Drawing inspiration from gradient-based meta-learning methods with infin...

0 Tristan Deleu, et al. ∙

∙ 02/28/2022

Bayesian Structure Learning with Generative Flow Networks

In Bayesian structure learning, we are interested in inferring a distrib...

7 Tristan Deleu, et al. ∙

∙ 01/27/2022

Rethinking Learning Dynamics in RL using Adversarial Networks

We present a learning mechanism for reinforcement learning of closely re...

54 Ramnath Kumar, et al. ∙

∙ 01/27/2022

The Effect of Diversity in Meta-Learning

Few-shot learning aims to learn representations that can tackle novel ta...

9 Ramnath Kumar, et al. ∙

∙ 11/17/2021

GFlowNet Foundations

Generative Flow Networks (GFlowNets) have been introduced as a method to...

40 Yoshua Bengio, et al. ∙

∙ 02/07/2021

Structured Sparsity Inducing Adaptive Optimizers for Deep Learning

The parameters of a neural network are naturally organized in groups, so...

10 Tristan Deleu, et al. ∙

∙ 02/19/2020

Curriculum in Gradient-Based Meta-Reinforcement Learning

Gradient-based meta-learners such as Model-Agnostic Meta-Learning (MAML)...

18 Bhairav Mehta, et al. ∙

∙ 10/18/2019

The TCGA Meta-Dataset Clinical Benchmark

Machine learning is bringing a paradigm shift to healthcare by changing ...

9 Mandana Samiei, et al. ∙

∙ 09/14/2019

Torchmeta: A Meta-Learning library for PyTorch

The constant introduction of standardized benchmarks in the literature h...

20 Tristan Deleu, et al. ∙

∙ 06/11/2019

Learning Powerful Policies by Using Consistent Dynamics Model

Model-based Reinforcement Learning approaches have the promise of being ...

10 Shagun Sodhani, et al. ∙

∙ 06/05/2019

Gradient-Based Neural DAG Learning

We propose a novel score-based approach to learning a directed acyclic g...

3 Sébastien Lachapelle, et al. ∙

∙ 01/30/2019

A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

We propose to meta-learn causal structures based on how fast a learner a...

0 Yoshua Bengio, et al. ∙

∙ 12/05/2018

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

The capacity of meta-learning algorithms to quickly adapt to a variety o...

0 Tristan Deleu, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world