9 papers
The Role of Coordinates in Pareto Regret for Adversarial Multi-Objective Bandits
Changkun Guan, Mengfan Xu
Adversarial multi-objective bandits hold the potential to help us optimize choices (arms) whose reward is a multidimensional vector chosen by an adversary and whose performance is…
Conformal-Style Quantile Analyses for Stochastic Bandits
Chengyu Du, Mengfan Xu
Stochastic bandit algorithms are usually analyzed under a mean-reward criterion, yet many problems favor arms with strong upper-tail performance, which we study herein. For a fixed…
Multi-Objective Multi-Agent Bandits: From Learning Efficiency to Fairness Optimization
John Wang, Mengfan Xu
We study multi-objective multi-agent multi-armed bandits (MO-MA-MAB) under stochastic rewards, where agents observe heterogeneous reward vectors and communicate over time-varying g…
Bandit Learning in General Open Multi-agent Systems
Mengfan Xu
Open systems, where agents arrive and depart over time, are growing in digital platforms. Existing work on online learning in such systems, however, often relies on structural assu…
Are Stochastic Multi-objective Bandits Harder than Single-objective Bandits?
Changkun Guan, Mengfan Xu
Multi-objective bandits have attracted increasing attention for their broad applicability, with \(d\)-dimensional reward vectors inducing Pareto regret. There has been a subtle deb…
Two-Sided Time-Independent Regret for Matching Markets with Limited Interviews
Amirmahdi Mirfakhar, Xuchuang Wang, Mengfan Xu +2
Two-sided matching platforms rely on preferences from both sides, yet participants can evaluate only a small fraction of potential partners. In practice, they use low-cost pre-matc…
Distributed Multi-Agent Bandits Over Erdős-Rényi Random Networks
Jingyuan Liu, Hao Qiu, Lin Yang +1
We study the distributed multi-agent multi-armed bandit problem with heterogeneous rewards over random communication graphs. Uniquely, at each time step agents communicate over…
Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models
Mengfan Xu, Liren Shan, Fatemeh Ghaffari +3
We study a novel heterogeneous multi-agent multi-armed bandit problem with a cluster structure induced by stochastic block models, influencing not only graph topology, but also rew…
Multi-agent Multi-armed Bandit with Fully Heavy-tailed Dynamics
Xingyu Wang, Mengfan Xu
We study decentralized multi-agent multi-armed bandits in fully heavy-tailed settings, where clients communicate over sparse random graphs with heavy-tailed degree distributions an…