1 citations · 1 across the 6 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2023
Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning
L. L. Ankile, B. S. Ham, K. Mao +4
When assisting human users in reinforcement learning (RL), we can represent users as RL agents and study key parameters, called \emph{user traits}, to inform intervention design. W…
cs.LG2022
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry
Mark Penrod, Harrison Termotto, Varshini Reddy +3
For responsible decision making in safety-critical settings, machine learning models must effectively detect and process edge-case data. Although existing works show that predictiv…
cs.LG2022
Policy Optimization with Sparse Global Contrastive Explanations
Jiayu Yao, Sonali Parbhoo, Weiwei Pan +1
We develop a Reinforcement Learning (RL) framework for improving an existing behavior policy via sparse, user-interpretable changes. Our goal is to make minimal changes while gaini…