Operations Research

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• Post Reinforcement Learning Inference AI翻译
  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...
• Trading Prophets AI翻译
  作者：Correa, Jose; Cristi, Andres; Dutting, Paul; Hajiaghayi, Mohammad; Olkowski, Jan; Schewiore, Kevin
  作者单位：Universidad de Chile; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Alphabet Inc.; Google Incorporated; University System of Maryland; University of Maryland College Park; University of Cologne; University of Southern Denmark
  摘要：In this work, we initiate the study of buy-and-sell prophet inequalities. We start by considering what is arguably the most fundamental setting. In this setting, the online algorithm observes a sequence of prices one after the other. At each time step, the online algorithm can decide to buy and pay the current price if it does not hold the item already, or it can decide to sell and collect the current price as a reward if it holds the item. We identify settings in which a constant-factor buy-a...
• A Stability Principle for Learning Under Nonstationarity AI翻译
  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...
• LEGO: Optimal Online Learning Under Sequential Price Competition AI翻译
  作者：Li, Shukai; Shi, Cong; Mehrotra, Sanjay
  作者单位：New York University; NYU Shanghai; University of Miami; Northwestern University
  摘要：We consider price competition among multiple sellers over a selling horizon of T periods. In each period, sellers simultaneously set prices and subsequently observe their own demand realizations, which are unobservable to competitors. The realized demand of each seller depends on the prices of all sellers and follows a private, unknown linear model. We propose a least-squares estimation and then gradient optimization (LEGO) policy, which does not require sellers to share demand information or ...
• Optimal Inventory Allocation for Indifferent Goods Under Dynamic Substitution AI翻译
  作者：Zhou, Ziyan; Wang, Tong; Zhang, Tingwei
  作者单位：Shanghai Jiao Tong University; City University of Hong Kong; The Chinese University of Hong Kong, Shenzhen
  摘要：Stockout-based substitution creates complex stochastic dynamics in inventory systems even in highly symmetric settings. We study a joint assortment and inventory allocation problem in which a firm allocates a fixed total inventory of m units across n perfectly substitutable product types (e.g., colors or designs). Customers are indifferent among available types and arrive sequentially, each purchasing one unit chosen uniformly at random from the nonempty types. The sales process terminates whe...
• Diagnosing Model Performance Under Distribution Shift AI翻译
  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...
• On the Convergence of Modified Policy Iteration in Risk-Sensitive Exponential Cost Markov Decision Processes AI翻译
  作者：Murthy, Yashaswini; Moharrami, Mehrdad; Srikant, Rayadurgam
  作者单位：California Institute of Technology; University of Iowa; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Modified policy iteration (MPI) is a dynamic programming algorithm that combines elements of policy iteration and value iteration. The convergence of MPI is wellstudied in the context of discounted and average-cost Markov decision processes (MDPs). In this work, we consider the exponential cost risk-sensitive MDP formulation, which is known to provide some robustness to model parameters. Although policy iteration and value iteration are well-studied in the context of risk-sensitive MDPs, MPI i...
• Package Bids in Combinatorial Electricity Auctions: Selection, Welfare Losses, and Alternatives AI翻译
  作者：Hubner, Thomas; Hug, Gabriela
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：A key challenge in combinatorial auctions is designing bid formats that accurately capture agents' preferences while remaining computationally feasible. This is especially true for electricity auctions, where complex preferences complicate straightforward solutions. In this context, we examine the XOR package bid, the default choice in combinatorial auctions and adopted in European day-ahead and intraday auctions under the name exclusive group of block bids. Unlike parametric bid formats often...
• Distributed Speed Scaling in Large-Scale Service Systems AI翻译
  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 ...
• Coverage-Validity-Aware Algorithmic Recourse AI翻译
  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...