most citedCLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning

6 citations · 9 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2023★ 6 cited

CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning

Sheng Yue, Guanbo Wang, Wei Shao +4

This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to exp…

cs.LG2022★ 3 cited

How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies

Edward Small, Wei Shao, Zeliang Zhang +4

With the introduction of machine learning in high-stakes decision making, ensuring algorithmic fairness has become an increasingly important problem to solve. In response to this,…