61 citations · 77 across the 14 of their papers we have counts for
6 papers · 1 filter
Agentic Unlearning: When LLM Agent Meets Machine Unlearning
Bin Wang, Fan Wang, Pingping Wang +5
In this paper, we introduce \textbf{agentic unlearning} which removes specified information from both model parameters and persistent memory in agents with closed-loop interaction.…
G-OSR: A Comprehensive Benchmark for Graph Open-Set Recognition
Yicong Dong, Rundong He, Guangyao Chen +4
Graph Neural Networks (GNNs) have achieved significant success in machine learning, with wide applications in social networks, bioinformatics, knowledge graphs, and other fields. M…
Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation
Fan Wang, Zhongyi Han, Xingbo Liu +2
In domain adaptation, there are two popular paradigms: Unsupervised Domain Adaptation (UDA), which aligns distributions using source data, and Source-Free Domain Adaptation (SFDA),…
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
Zhongyi Han, Guanglin Zhou, Rundong He +7
In machine learning, generalization against distribution shifts -- where deployment conditions diverge from the training scenarios -- is crucial, particularly in fields like climat…
Active Source Free Domain Adaptation
Fan Wang, Zhongyi Han, Zhiyan Zhang +1
Source free domain adaptation (SFDA) aims to transfer a trained source model to the unlabeled target domain without accessing the source data. However, the SFDA setting faces an ef…
Learning to Rectify for Robust Learning with Noisy Labels
Haoliang Sun, Chenhui Guo, Qi Wei +2
Label noise significantly degrades the generalization ability of deep models in applications. Effective strategies and approaches, \textit{e.g.} re-weighting, or loss correction, a…