Jacob Buckman | DeepAI

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∙ 11/14/2022

Deep Autoregressive Regression

In this work, we demonstrate that a major limitation of regression using...

0 Adam Khakhar, et al. ∙

∙ 06/02/2022

When does return-conditioned supervised learning work for offline reinforcement learning?

Several recent works have proposed a class of algorithms for the offline...

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. ∙

∙ 09/15/2020

The Importance of Pessimism in Fixed-Dataset Policy Optimization

We study worst-case guarantees on the expected return of fixed-dataset p...

4 Jacob Buckman, et al. ∙

∙ 06/06/2019

DeepMDP: Learning Continuous Latent Space Models for Representation Learning

Many reinforcement learning (RL) tasks provide the agent with high-dimen...

4 Carles Gelada, et al. ∙

∙ 07/04/2018

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

Integrating model-free and model-based approaches in reinforcement learn...

6 Jacob Buckman, et al. ∙

∙ 03/13/2018

Neural Lattice Language Models

In this work, we propose a new language modeling paradigm that has the a...

0 Jacob Buckman, et al. ∙

∙ 02/23/2018

Is Generator Conditioning Causally Related to GAN Performance?

Recent work (Pennington et al, 2017) suggests that controlling the entir...

0 Augustus Odena, et al. ∙

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