6 citations · 9 across the 8 of their papers we have counts for
9 papers
Towards Optimal Inventory Control under Censored Demand: A Biased Sample-Average Approximation Approach
Yuxuan Han, Xiaoyu Fan, Jiawei Zhang +1
We study data-driven multi-period lost-sales inventory control under censored demand, where a stockout reveals only that demand exceeded the stocking level. We develop a unified, m…
Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning
Yingying Fan, Yuxuan Han, Jinchi Lv +2
We study contextual dynamic pricing in a semiparametric scalar-index valuation model where the latent value is , with an unknown utility map a…
Optimal Online and Offline Algorithms for Contextual MNL with Applications to Assortment and Pricing
Yunfan Zhang, Yuxuan Han, Hongyu Shan +2
Selecting which products to display and at what prices is a central decision in retail and e-commerce operations. In many applications, these two choices must be made jointly under…
RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model
Junyi Fan, Yuxuan Han, Jialin Zeng +4
Efficiently learning equilibria with large state and action spaces in general-sum Markov games while overcoming the curse of multi-agency is a challenging problem. Recent works hav…
Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oracles
Yuxuan Han, Jialin Zeng, Yang Wang +2
We study the stochastic contextual bandit with knapsacks (CBwK) problem, where each action, taken upon a context, not only leads to a random reward but also costs a random resource…
Performance Analysis of OMP in Super-Resolution
Yuxuan Han, Zhiyi Huang, Yang Wang +1
Given a spectrally sparse signal consisting of complex sinusoids, we consider the super-resolution problem,…
On Private Online Convex Optimization: Optimal Algorithms in -Geometry and High Dimensional Contextual Bandits
Yuxuan Han, Zhicong Liang, Zhipeng Liang +3
Differentially private (DP) stochastic convex optimization (SCO) is ubiquitous in trustworthy machine learning algorithm design. This paper studies the DP-SCO problem with streamin…
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup
Bingzhe Wu, Zhipeng Liang, Yuxuan Han +3
Recently, federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. Nevertheless, di…
Generalized Linear Bandits with Local Differential Privacy
Yuxuan Han, Zhipeng Liang, Yang Wang +1
Contextual bandit algorithms are useful in personalized online decision-making. However, many applications such as personalized medicine and online advertising require the utilizat…