6 citations · 6 across the 2 of their papers we have counts for
3 papers
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.AI2025
Reinforced Reasoning for Embodied Planning
Di Wu, Jiaxin Fan, Junzhe Zang +4
Embodied planning requires agents to make coherent multi-step decisions based on dynamic visual observations and natural language goals. While recent vision-language models (VLMs)…
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…