2.4k citations · 12.3k across the 115 of their papers we have counts for
4 papers · 2 filters
Is High Variance Unavoidable in RL? A Case Study in Continuous Control
Johan Bjorck, Carla P. Gomes, Kilian Q. Weinberger
Reinforcement learning (RL) experiments have notoriously high variance, and minor details can have disproportionately large effects on measured outcomes. This is problematic for cr…
Online Adaptation to Label Distribution Shift
Ruihan Wu, Chuan Guo, Yi Su +1
Machine learning models often encounter distribution shifts when deployed in the real world. In this paper, we focus on adaptation to label distribution shift in the online setting…
Towards Deeper Deep Reinforcement Learning with Spectral Normalization
Johan Bjorck, Carla P. Gomes, Kilian Q. Weinberger
In computer vision and natural language processing, innovations in model architecture that increase model capacity have reliably translated into gains in performance. In stark cont…
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision
Johan Bjorck, Xiangyu Chen, Christopher De Sa +2
Low-precision training has become a popular approach to reduce compute requirements, memory footprint, and energy consumption in supervised learning. In contrast, this promising ap…