Guangyan Gan — accepted AI-conference papers

2026

Fairness-Aware Design for Contextual Experiments: Guaranteeing Reliability and Equity in Heterogeneous Subgroups

Guangyan Gan, Ling Zhang, Yanhua Cheng, Yongxiang Tang, Kaiyuan Li, Xialong Liu +1

AAAI 2026technical

Experimental design is critical for evidence-based decision-making in healthcare, marketing, and public policy. However, designing efficient experiments across heterogeneous subgroups presents significant challenges. Existing methods often optimize for statistical power or overall sample efficiency,

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2026

Privacy-Preserving Reinforcement Learning with One-Sided Feedback

Lin Cong, Guangyan Gan, Hanzhang Qin, Zhenzhen Yan

IJCAI 2026

We study reinforcement learning (RL) in multi-dimensional continuous state and action spaces with one-sided feedback, where the agent receives partial observations of the state and obtains reward information for only a subset of the state-action space at each time step. This setting introduces subst

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