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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...
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11/02/2022
Behavior Prior Representation learning for Offline Reinforcement Learning
Offline reinforcement learning (RL) struggles in environments with rich ...
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11/01/2022
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Goal-conditioned reinforcement learning (RL) is a promising direction fo...
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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...
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06/10/2022
Measuring the Carbon Intensity of AI in Cloud Instances
By providing unprecedented access to computational resources, cloud comp...
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06/02/2022
Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning
Most theoretically motivated work in the offline reinforcement learning ...
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05/27/2022
Non-Markovian policies occupancy measures
A central object of study in Reinforcement Learning (RL) is the Markovia...
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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...
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02/10/2021
On the Regularity of Attention
Attention is a powerful component of modern neural networks across a wid...
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10/02/2020
A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms
We investigate the discounting mismatch in actor-critic algorithm implem...
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09/11/2020
Adversarial score matching and improved sampling for image generation
Denoising score matching with Annealed Langevin Sampling (DSM-ALS) is a ...
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07/06/2020
A Mathematical Theory of Attention
Attention is a powerful component of modern neural networks across a wid...
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06/12/2020
Deep Reinforcement and InfoMax Learning
Our work is based on the hypothesis that a model-free agent whose repres...
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03/10/2020
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Adversarial learning has demonstrated good performance in the unsupervis...
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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...
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09/11/2019
Safe Policy Improvement with an Estimated Baseline Policy
Previous work has shown the unreliability of existing algorithms in the ...
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07/11/2019
Safe Policy Improvement with Soft Baseline Bootstrapping
Batch Reinforcement Learning (Batch RL) consists in training a policy us...
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01/27/2019
On Learning Invariant Representation for Domain Adaptation
Due to the ability of deep neural nets to learn rich representations, re...
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12/12/2018
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Inspired by the phenomenon of catastrophic forgetting, we investigate th...
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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...
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09/07/2018
Learning Invariances for Policy Generalization
While recent progress has spawned very powerful machine learning systems...
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06/29/2018
