4 papers · 1 filter
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization
Mengxiao Zhang, Ramiro Deo-Campo Vuong, Haipeng Luo
We consider the problem of online multi-agent Nash social welfare (NSW) maximization. While previous works of Hossain et al. [2021], Jones et al. [2023] study similar problems in s…
Provably Efficient Interactive-Grounded Learning with Personalized Reward
Mengxiao Zhang, Yuheng Zhang, Haipeng Luo +1
Interactive-Grounded Learning (IGL) [Xie et al., 2021] is a powerful framework in which a learner aims at maximizing unobservable rewards through interacting with an environment an…
Contextual Multinomial Logit Bandits with General Value Functions
Mengxiao Zhang, Haipeng Luo
Contextual multinomial logit (MNL) bandits capture many real-world assortment recommendation problems such as online retailing/advertising. However, prior work has only considered…
Efficient Contextual Bandits with Uninformed Feedback Graphs
Mengxiao Zhang, Yuheng Zhang, Haipeng Luo +1
Bandits with feedback graphs are powerful online learning models that interpolate between the full information and classic bandit problems, capturing many real-life applications. A…