Jiahui Li - RankMe

18 papers A* 10Journal 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Path-Decoupled Hyperbolic Flow Matching for Few-Shot Adaptation. CoRR Lin Li, Ziqi Jiang, Gefan Ye, Zhenqi He, Jiahui Li, Jun Xiao, Kwang-Ting Cheng, Long Chen
2026	A*	`conf`	Relation-R1: Progressively Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relation Comprehension. AAAI Lin Li, Wei Chen, Jiahui Li, Kwang-Ting Cheng, Long Chen
2025	A*	`conf`	Learning Causal Transition Matrix for Instance-dependent Label Noise. AAAI Jiahui Li, Tai-Wei Chang, Kun Kuang, Ximing Li, Long Chen, Jun Zhou
2025	A*	`conf`	RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution. EMNLP Jiahui Li, Lin Li, Tai-Wei Chang, Kun Kuang, Long Chen, Jun Zhou, Cheng Yang
2025	J	`jnl`	Relation-R1: Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relational Comprehension. CoRR Lin Li, Wei Chen, Jiahui Li, Long Chen
2025	A*	`conf`	Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards. CVPR Zijing Hu, Fengda Zhang, Long Chen, Kun Kuang, Jiahui Li, Kaifeng Gao, Jun Xiao, Xin Wang, Wenwu Zhu
2025	J	`jnl`	Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards. CoRR Zijing Hu, Fengda Zhang, Long Chen, Kun Kuang, Jiahui Li, Kaifeng Gao, Jun Xiao, Xin Wang, Wenwu Zhu
2024	J	`jnl`	Learning Causal Transition Matrix for Instance-dependent Label Noise. CoRR Jiahui Li, Tai-Wei Chang, Kun Kuang, Ximing Li, Long Chen, Jun Zhou
2024	A*	`conf`	Optimizing Language Models with Fair and Stable Reward Composition in Reinforcement Learning. EMNLP Jiahui Li, Hanlin Zhang, Fengda Zhang, Tai-Wei Chang, Kun Kuang, Long Chen, Jun Zhou
2024	J	`jnl`	R3HF: Reward Redistribution for Enhancing Reinforcement Learning from Human Feedback. CoRR Jiahui Li, Tai-Wei Chang, Fengda Zhang, Kun Kuang, Long Chen
2023	A*	`conf`	Two Heads are Better Than One: A Simple Exploration Framework for Efficient Multi-Agent Reinforcement Learning. NeurIPS Jiahui Li, Kun Kuang, Baoxiang Wang, Xingchen Li, Fei Wu, Jun Xiao, Long Chen
2022	A*	`conf`	Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning. ICML Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Changjie Fan, Fei Wu, Jun Xiao
2022	A*	`conf`	S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning? KDD Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu
2022	J	`jnl`	S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning? CoRR Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu
2021	A*	`conf`	Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation. ACM Multimedia Jiahui Li, Kun Kuang, Lin Li, Long Chen, Songyang Zhang, Jian Shao, Jun Xiao
2021	J	`jnl`	Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation. CoRR Jiahui Li, Kun Kuang, Lin Li, Long Chen, Songyang Zhang, Jian Shao, Jun Xiao
2021	A*	`conf`	Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning. KDD Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, Jun Xiao
2021	J	`jnl`	Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning. CoRR Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, Jun Xiao