Yancheng Chen — accepted AI-conference papers

2026

Distribution-Aware Energy Minimization: Physical-Inspired Efficient Active Learning and Quantum Potentials

Zhicheng Yao, Wenguo Yang, Yancheng Chen, Dun Ma, Shengminjie Chen, Xiaoming Sun

IJCAI 2026

Active learning aims to maximize model performance with minimal annotation costs by selecting the most informative samples from large unlabeled pools, which often face a budget dilemma: uncertainty-based methods induce redundancy under low budgets, while representativeness-based methods struggle to

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2026

Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective

Yancheng Chen, Dun Ma, Shuai Zhang, Yang Liu, Xixun Lin, Xiangyu Zhao +3

ICML 2026poster

Graph Foundation Models (GFMs), built upon the *Pre-training and Adaptation* paradigm, have emerged as a research hotspot in graph learning. For GNN-based GFMs, graph prompt tuning has become the prevailing adaptation method for downstream tasks. Although recent methods explain why graph prompt tuni…

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