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

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

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cs.LG2025

Gains: Fine-grained Federated Domain Adaptation in Open Set

Zhengyi Zhong, Wenzheng Jiang, Weidong Bao +5

Conventional federated learning (FL) assumes a closed world with a fixed total number of clients. In contrast, new clients continuously join the FL process in real-world scenarios,…

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…