21 citations · 67 across the 15 of their papers we have counts for
15 papers
Exploration-free Algorithms for Multi-group Mean Estimation
Ziyi Wei, Huaiyang Zhong, Xiaocheng Li
We address the problem of multi-group mean estimation, which seeks to allocate a finite sampling budget across multiple groups to obtain uniformly accurate estimates of their means…
Understanding Uncertainty Sampling via Equivalent Loss
Shang Liu, Xiaocheng Li
Uncertainty sampling is a classical active-learning strategy, yet the statistical objective induced by its query rule is often implicit. We introduce the equivalent loss, whose gra…
Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming
Chunlin Sun, Shang Liu, Xiaocheng Li
In this paper, we study the predict-then-optimize problem where the output of a machine learning prediction task is used as the input of some downstream optimization problem, say,…
Learning from Stochastically Revealed Preference
John R. Birge, Xiaocheng Li, Chunlin Sun
We study the learning problem of revealed preference in a stochastic setting: a learner observes the utility-maximizing actions of a set of agents whose utility follows some unknow…
Non-stationary Bandits with Knapsacks
Shang Liu, Jiashuo Jiang, Xiaocheng Li
In this paper, we study the problem of bandits with knapsacks (BwK) in a non-stationary environment. The BwK problem generalizes the multi-arm bandit (MAB) problem to model the res…
Online Bin Packing with Known T
Shang Liu, Xiaocheng Li
In the online bin packing problem, a sequence of items is revealed one at a time, and each item must be packed into an available bin instantly upon its arrival. In this paper, we r…
On Dynamic Pricing with Covariates
Hanzhao Wang, Kalyan Talluri, Xiaocheng Li
We consider dynamic pricing with covariates under a generalized linear demand model: a seller can dynamically adjust the price of a product over a horizon of time periods, and…
Fairer LP-based Online Allocation via Analytic Center
Guanting Chen, Xiaocheng Li, Yinyu Ye
In this paper, we consider an online resource allocation problem where a decision maker accepts or rejects incoming customer requests irrevocably in order to maximize expected rewa…
The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks
Xiaocheng Li, Chunlin Sun, Yinyu Ye
In this paper, we study the bandits with knapsacks (BwK) problem and develop a primal-dual based algorithm that achieves a problem-dependent logarithmic regret bound. The BwK probl…
An Improved Analysis of LP-based Control for Revenue Management
Guanting Chen, Xiaocheng Li, Yinyu Ye
In this paper, we study a class of revenue management problems where the decision maker aims to maximize the total revenue subject to budget constraints on multiple type of resourc…
Online Stochastic Optimization with Wasserstein Based Non-stationarity
Jiashuo Jiang, Xiaocheng Li, Jiawei Zhang
We consider a general online stochastic optimization problem with multiple budget constraints over a horizon of finite time periods. In each time period, a reward function and mult…
Simple and Fast Algorithm for Binary Integer and Online Linear Programming
Xiaocheng Li, Chunlin Sun, Yinyu Ye
In this paper, we develop a simple and fast online algorithm for solving a class of binary integer linear programs (LPs) arisen in general resource allocation problem. The algorith…
Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds
Xiaocheng Li, Yinyu Ye
We study an online linear programming (OLP) problem under a random input model in which the columns of the constraint matrix along with the corresponding coefficients in the object…
Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans
Huaiyang Zhong, Xiaocheng Li, David Lobell +2
Eradicating hunger and malnutrition is a key development goal of the 21st century. We address the problem of optimally identifying seed varieties to reliably increase crop yield wi…
Quantile Markov Decision Process
Xiaocheng Li, Huaiyang Zhong, Margaret L. Brandeau
The goal of a traditional Markov decision process (MDP) is to maximize expected cumulative reward over a defined horizon (possibly infinite). In many applications, however, a decis…