4 papers
Efficient Online Proportional Sampling with Applications to Smoothed Online Learning
Amirmahdi Mirfakhar, Maria-Florina Balcan, Hedyeh Beyhaghi
We study the problem of efficient online proportional sampling from a high-dimensional domain under a -smoothed adversary, where the sampling distribution is induced by a dynami…
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…
Heterogeneous Multi-Agent Bandits with Parsimonious Hints
Amirmahdi Mirfakhar, Xuchuang Wang, Jinhang Zuo +2
We study a hinted heterogeneous multi-agent multi-armed bandits problem (HMA2B), where agents can query low-cost observations (hints) in addition to pulling arms. In this framework…
Online Fair Revenue Maximizing Cake Division with Non-Contiguous Pieces in Adversarial Bandits
Mohammad Ghodsi, Amirmahdi Mirfakhar
The classic cake-cutting problem provides a model for addressing the fair and efficient allocation of a divisible, heterogeneous resource among agents with distinct preferences. Fo…