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作者：Tsang, Man Yiu; Shehadeh, Karmel S.
作者单位：Texas Tech University System; Texas Tech University; University of Southern California
摘要：We propose a new framework that unifies different fairness measures into a general, parameterized class of convex fairness measures suitable for optimization contexts. First, we propose a new class of order-based fairness measures, discuss their properties, and derive an axiomatic characterization for such measures. Then, we introduce the class of convex fairness measures, discuss their properties, and derive an equivalent dual representation of these measures as a robustified order-based fair...
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作者：Song, Jing-Sheng; Xiao, Li; Zhang, Hanqin
作者单位：Duke University; University of Macau; National University of Singapore
摘要：This study explores the effective use of order-tracking information in dualsourcing inventory systems in both backlogging and lost-sales settings. Our inventory model features a normal source, comprising a two-stage tandem queue with Erlangdistributed processing times at each stage, and an emergency source that bypasses the first stage. We show that under certain conditions the optimal policy is characterized by two thresholds and one switching curve determined by the workload at the emergency...
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作者：Wang, Jiaqi; Xie, Weijun; Ryzhov, Ilya O.; Markovic, Nikola; Ou, Ge
作者单位：University System of Maryland; University of Maryland College Park; University System of Georgia; Georgia Institute of Technology; University System of Maryland; University of Maryland College Park; Utah System of Higher Education; University of Utah; State University System of Florida; University of Florida
摘要：Immediately following a major earthquake, reconnaissance surveys seek to assess structural damage throughout the region with the help of a limited number of on-ground inspections. The goal is to collect informative and representative data that will guide subsequent relief efforts. We formulate a new type of vehicle routing problem, in which vehicles are tasked with data collection, and the objective function measures data quality using a nonlinear, nonseparable experimental design criterion. W...
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作者：Feldman, Michal; Gkatzelis, Vasilis; Gravin, Nick; Schoepflin, Daniel
作者单位：Tel Aviv University; Drexel University; Shanghai University of Finance & Economics; Rutgers University System; Rutgers University New Brunswick
摘要：In a single-parameter mechanism design problem, a provider is looking to sell some service to a group of potential buyers. Each buyer i has a private value vi for receiving this service, but a feasibility constraint restricts which buyers can be simultaneously served. Recent work in economics introduced (deferred-acceptance) clock auctions as a superior class of auctions for this problem due to their transparency, simplicity, and strong incentive guarantees. Subsequent work focused on evaluati...
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作者：Syrgkanis, Vasilis; Zhan, Ruohan
作者单位：Stanford University; University of London; University College London
摘要：We study estimation and inference using data collected by reinforcement learning (RL) algorithms. These algorithms adaptively experiment by interacting with individual units over multiple stages, updating their strategies based on past outcomes. Our goal is to evaluate a counterfactual policy after data collection and estimate structural parameters, such as dynamic treatment effects, that support credit assignment and quantify the impact of early actions on final outcomes. These parameters can...
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作者：Huang, Chengpiao; Wang, Kaizheng
作者单位：Columbia University; Columbia University
摘要：We develop a versatile framework for statistical learning in nonstationary environments. In each time period, our approach applies a stability principle to select a look-back window that maximizes the utilization of historical data while keeping the cumulative bias within an acceptable range relative to the stochastic error. Our theory showcases the adaptivity of this approach to unknown nonstationarity. We prove regret bounds that are minimax optimal up to logarithmic factors when the populat...
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作者：Cai, Tiffany (Tianhui); Namkoong, Hongseok; Yadlowsky, Steve
作者单位：Columbia University; Columbia University; Alphabet Inc.; DeepMind
摘要：This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact permissions@informs.org. The Publisher does not warrant or guarantee the article's accuracy, completeness, merchantability, fitness inclusion of an advertisement in this article, neither constitutes nor implies a guaran...
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作者：Rutten, Daan; Zubeldia, Martin; Mukherjee, Debankur
作者单位：University System of Georgia; Georgia Institute of Technology; University of Minnesota System; University of Minnesota Twin Cities
摘要：We consider a large-scale parallel-server loss system with an unknown arrival rate, where each server is able to adjust its processing speed. The objective is to minimize the system cost, which consists of a power cost to maintain the servers' processing speeds and a quality of service cost depending on the tasks' processing times among others. We draw on ideas from stochastic approximation to design a novel speed-scaling algorithm and prove that the servers' processing speeds converge to the ...
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作者：Bui, Ngoc; Nguyen, Duy; Yue, Man-Chung; Nguyen, Viet Anh
作者单位：Yale University; University of North Carolina; University of North Carolina Chapel Hill; University of Hong Kong; Chinese University of Hong Kong
摘要：Algorithmic recourse emerges as a prominent technique to promote the explainability, transparency, and ethics of machine learning models. Existing algorithmic recourse approaches often assume an invariant predictive model; however, the predictive model is usually updated on the arrival of new data. Thus, a recourse that is valid respective to the present model may become in valid for the future model. To resolve this issue, we propose a novel framework to generate a model-agnostic recourse tha...
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作者：Susan, Fransisca; Golrezaei, Negin; Emamjomeh-Zadeh, Ehsan; Kempe, David
作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of Southern California
摘要：We study the problem of actively learning a nonparametric choice model based on consumers' decisions. We present a negative result showing that such choice models may not be identifiable. To overcome the identifiability problem, we introduce a directed acyclic graph (DAG) representation of the choice model. This representation provably encodes all the information about the choice model that can be inferred from the available data, in the sense that it permits computing all choice probabilities...
