5 papers
In-Context Learning for Data-Driven Censored Inventory Control
Sohom Mukherjee, Anh-Duy Pham, Richard Pibernik +1
We study inventory control with decision-dependent censoring, focusing on the censored or repeated newsvendor (R-NV), where each order quantity determines whether demand is fully o…
Autoregressive Learning in Joint KL: Sharp Oracle Bounds and Lower Bounds
Yunbei Xu, Yuzhe Yuan, Ruohan Zhan
We study the fundamental and timely problem of learning long sequences in autoregressive modeling and next-token prediction under model misspecification, measured by the joint Kull…
Thompson Sampling for Repeated Newsvendor
Li Chen, Hanzhang Qin, Yunbei Xu +2
In this paper, we investigate the performance of Thompson Sampling (TS) for online learning with censored feedback, focusing primarily on the classic repeated newsvendor model--a f…
Finite-Time Minimax Bounds and an Optimal Lyapunov Policy in Queueing Control
Yujie Liu, Vincent Y. F. Tan, Yunbei Xu
We introduce an original minimax framework for finite-time performance analysis in queueing control and propose a surprisingly simple Lyapunov-based scheduling policy with superior…
Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability
Fan Chen, Dylan J. Foster, Yanjun Han +3
We develop a unifying framework for information-theoretic lower bound in statistical estimation and interactive decision making. Classical lower bound techniques -- such as Fano's…