Remi Tachet des Combes | DeepAI

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∙ 06/22/2023

Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting

Most offline reinforcement learning (RL) algorithms return a target poli...

0 Zhang-Wei Hong, et al. ∙

∙ 11/02/2022

Behavior Prior Representation learning for Offline Reinforcement Learning

Offline reinforcement learning (RL) struggles in environments with rich ...

0 Hongyu Zang, et al. ∙

∙ 11/01/2022

Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning

Goal-conditioned reinforcement learning (RL) is a promising direction fo...

0 Riashat Islam, et al. ∙

∙ 10/31/2022

Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information

Learning to control an agent from data collected offline in a rich pixel...

0 Riashat Islam, et al. ∙

∙ 06/10/2022

Measuring the Carbon Intensity of AI in Cloud Instances

By providing unprecedented access to computational resources, cloud comp...

0 Jesse Dodge, et al. ∙

∙ 06/02/2022

Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning

Most theoretically motivated work in the offline reinforcement learning ...

0 David Brandfonbrener, et al. ∙

∙ 05/27/2022

Non-Markovian policies occupancy measures

A central object of study in Reinforcement Learning (RL) is the Markovia...

0 Romain Laroche, et al. ∙

∙ 04/08/2021

A single gradient step finds adversarial examples on random two-layers neural networks

Daniely and Schacham recently showed that gradient descent finds adversa...

11 Sébastien Bubeck, et al. ∙

∙ 02/10/2021

On the Regularity of Attention

Attention is a powerful component of modern neural networks across a wid...

0 James Vuckovic, et al. ∙

∙ 10/02/2020

A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms

We investigate the discounting mismatch in actor-critic algorithm implem...

1 Shangtong Zhang, et al. ∙

∙ 09/11/2020

Adversarial score matching and improved sampling for image generation

Denoising score matching with Annealed Langevin Sampling (DSM-ALS) is a ...

10 Alexia Jolicoeur-Martineau, et al. ∙

∙ 07/06/2020

A Mathematical Theory of Attention

Attention is a powerful component of modern neural networks across a wid...

0 James Vuckovic, et al. ∙

∙ 06/12/2020

Deep Reinforcement and InfoMax Learning

Our work is based on the hypothesis that a model-free agent whose repres...

0 Bogdan Mazoure, et al. ∙

∙ 03/10/2020

Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift

Adversarial learning has demonstrated good performance in the unsupervis...

18 Remi Tachet des Combes, et al. ∙

∙ 11/14/2019

A Reduction from Reinforcement Learning to No-Regret Online Learning

We present a reduction from reinforcement learning (RL) to no-regret onl...

0 Ching-An Cheng, et al. ∙

∙ 09/11/2019

Safe Policy Improvement with an Estimated Baseline Policy

Previous work has shown the unreliability of existing algorithms in the ...

0 Thiago D. Simão, et al. ∙

∙ 07/11/2019

Safe Policy Improvement with Soft Baseline Bootstrapping

Batch Reinforcement Learning (Batch RL) consists in training a policy us...

0 Kimia Nadjahi, et al. ∙

∙ 01/27/2019

On Learning Invariant Representation for Domain Adaptation

Due to the ability of deep neural nets to learn rich representations, re...

0 Han Zhao, et al. ∙

∙ 12/12/2018

An Empirical Study of Example Forgetting during Deep Neural Network Learning

Inspired by the phenomenon of catastrophic forgetting, we investigate th...

14 Mariya Toneva, et al. ∙

∙ 09/18/2018

On the Learning Dynamics of Deep Neural Networks

While a lot of progress has been made in recent years, the dynamics of l...

0 Remi Tachet des Combes, et al. ∙

∙ 09/07/2018

Learning Invariances for Policy Generalization

While recent progress has spawned very powerful machine learning systems...

0 Remi Tachet des Combes, et al. ∙

∙ 06/29/2018

Counting to Explore and Generalize in Text-based Games

We propose a recurrent RL agent with an episodic exploration mechanism t...

0 Xingdi Yuan, et al. ∙

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