SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition

no code implementations • 29 Sep 2021 • Dylan Z Slack, Yinlam Chow, Bo Dai, Nevan Wichers

Though many reinforcement learning (RL) problems involve learning policies in settings that are difficult to specify safety constraints and sparse rewards, current methods struggle to rapidly and safely acquire successful policies.

reinforcement-learning Reinforcement Learning (RL) +2

Defuse: Debugging Classifiers Through Distilling Unrestricted Adversarial Examples

no code implementations • 1 Jan 2021 • Dylan Z Slack, Nathalie Rauschmayr, Krishnaram Kenthapadi

As a route to better discover and fix model bugs, we propose failure scenarios: regions on the data manifold that are incorrectly classified by a model.

Clustering