143 citations · 285 across the 7 of their papers we have counts for
8 papers
Neural Regression For Scale-Varying Targets
Adam Khakhar, Jacob Buckman
In this work, we demonstrate that a major limitation of regression using a mean-squared error loss is its sensitivity to the scale of its targets. This makes learning settings cons…
When does return-conditioned supervised learning work for offline reinforcement learning?
David Brandfonbrener, Alberto Bietti, Jacob Buckman +2
Several recent works have proposed a class of algorithms for the offline reinforcement learning (RL) problem that we will refer to as return-conditioned supervised learning (RCSL).…
Non-Markovian policies occupancy measures
Romain Laroche, Remi Tachet des Combes, Jacob Buckman
A central object of study in Reinforcement Learning (RL) is the Markovian policy, in which an agent's actions are chosen from a memoryless probability distribution, conditioned onl…
The Importance of Pessimism in Fixed-Dataset Policy Optimization
Jacob Buckman, Carles Gelada, Marc G. Bellemare
We study worst-case guarantees on the expected return of fixed-dataset policy optimization algorithms. Our core contribution is a unified conceptual and mathematical framework for…
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada, Saurabh Kumar, Jacob Buckman +2
Many reinforcement learning (RL) tasks provide the agent with high-dimensional observations that can be simplified into low-dimensional continuous states. To formalize this process…
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Jacob Buckman, Danijar Hafner, George Tucker +2
Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity. H…
Neural Lattice Language Models
Jacob Buckman, Graham Neubig
In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural latti…
Is Generator Conditioning Causally Related to GAN Performance?
Augustus Odena, Jacob Buckman, Catherine Olsson +4
Recent work (Pennington et al, 2017) suggests that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning. Motivated…