Qirun Zeng

I am a Ph.D. student in computer science at City University of Hong Kong, advised by Prof. Jinhang Zuo. I study theoretical questions in sequential decision-making, with current work on bandit learning, hybrid feedback, robustness, and influence maximization.

Research Positioning

Broadly, my work is in theoretical computer science. Within that area, I focus on learning theory and online decision-making, with a secondary algorithm-design thread in influence maximization.

Selected Work

A short view of current and recent projects. See the research page for the full list.

Best Arm Identification in Generalized Linear Bandits via Hybrid Feedback

Qirun Zeng , Xuchuang Wang , Jiayi Shen , Xutong Liu , Fang Kong , Jinhang Zuo

Studies best-arm identification in generalized linear bandits with hybrid feedback.

arXiv Code

One Rounding Fits All: Memory-Efficient Approximation Algorithms for Partition-Constrained Influence Maximization

Qixin Zhang^* , Qirun Zeng^* , Hui Lu , Pingchuan Ma , Jinhang Zuo , Renqiang Luo , Yi Yu , Dacheng Tao

Studies memory-efficient approximation algorithms for influence maximization under partition constraints.

KDD'2026 Code

Fusing Reward and Dueling Feedback in Stochastic Bandits

Xuchuang Wang , Qirun Zeng , Jinhang Zuo , Xutong Liu , Mohammad Hajiesmaili , John C. S. Lui , Adam Wierman

Examines stochastic bandits with both reward observations and dueling feedback.

ICML'2025 Code

View all publications

Contact

For research discussions, seminar invitations, and collaboration inquiries, email is the most reliable contact channel.