12 papers
Optimal Nonparametric Dynamic Pricing with Censored Demand and Adversarial Inventory
Mengxiao Zhang, Yingfei Wang, Haipeng Luo
We study online dynamic pricing with censored demand, where an arbitrary inventory level is revealed before pricing and may adapt to past observations, while demand follows an unkn…
Adaptive Calibration in Non-Stationary Environments
Junyan Liu, Haipeng Luo, Lillian J. Ratliff
Making calibrated online predictions is a central challenge in modern AI systems. Much of the existing literature focuses on fully adversarial environments where outcomes may be ar…
Near-Optimal Last-Iterate Convergence for Zero-Sum Games with Bandit Feedback and Opponent Actions
Soumita Hait, Ping Li, Haipeng Luo +1
Last-iterate convergence of learning dynamics in games has attracted significant recent attention. In two-player zero-sum games with bandit feedback, where only the loss of the sel…
Interaction-Grounded Learning for Contextual Markov Decision Processes with Personalized Feedback
Mengxiao Zhang, Yuheng Zhang, Haipeng Luo +1
In this paper, we study Interaction-Grounded Learning (IGL) [Xie et al., 2021], a paradigm designed for realistic scenarios where the learner receives indirect feedback generated b…
Comparator-Adaptive -Regret: Improved Bounds, Simpler Algorithms, and Applications to Games
Soumita Hait, Ping Li, Haipeng Luo +1
In the classic expert problem, -regret measures the gap between the learner's total loss and that achieved by applying the best action transformation . A recent work by…
Contextual Linear Bandits with Delay as Payoff
Mengxiao Zhang, Yingfei Wang, Haipeng Luo
A recent work by Schlisselberg et al. (2024) studies a delay-as-payoff model for stochastic multi-armed bandits, where the payoff (either loss or reward) is delayed for a period th…
Alternating Regret for Online Convex Optimization
Soumita Hait, Ping Li, Haipeng Luo +1
Motivated by alternating learning dynamics in two-player games, a recent work by Cevher et al.(2024) shows that alternating regret is possible for any -round adver…
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…
Online Learning in Contextual Second-Price Pay-Per-Click Auctions
Mengxiao Zhang, Haipeng Luo
We study online learning in contextual pay-per-click auctions where at each of the rounds, the learner receives some context along with a set of ads and needs to make an estima…