5期

最新文章

• Multi-item Online Order Fulfillment in a Two-Layer Network AI翻译
  作者：Zhao, Yanyang; Wang, Xinshang; Xin, Linwei
  作者单位：University of Chicago; Alibaba Group
  摘要：The global e-commerce boom has driven rapid expansion of fulfillment infrastructure, with e-retailers building more warehouses to offer faster deliveries. However, fulfillment costs have surged over the past decade. This paper addresses the problem of minimizing these costs, where an e-retailer must decide in real time which warehouse(s) will fulfill each order, considering inventory constraints. Orders can be split among warehouses at an additional cost. We focus on a regional distribution ce...
• Policy Learning with Competing Agents AI翻译
  作者：Sahoo, Roshni; Wager, Stefan
  作者单位：Stanford University; Stanford University
  摘要：Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat. When agents can respond strategically to such policies, competition arises, complicating estimation of the optimal policy. In this paper, we study capacity-constrained treatment assignments in the presence of such interference. We consider a dynamic model in which the decision maker allocates treatments at each time step and heterogeneous agents myopically b...
• Blind Network Revenue Management and Bandits with Knapsacks Under Limited Switches AI翻译
  作者：Simchi-Levi, David; Xu, Yunzong; Zhao, Jinglong
  作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of Illinois System; University of Illinois Urbana-Champaign; Boston University
  摘要：This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributionally unknown demand, and must allocate finite initial inventory across multiple resources over time. In addition to standard resource constraints, we impose a switching constrai...
• Instrumenting While Experimenting: An Empirical Method for Competitive Pricing at Scale AI翻译
  作者：Jiang, Zhaohui (Zoey); Li, Jun
  作者单位：Carnegie Mellon University; University of Michigan System; University of Michigan
  摘要：Accurate operational decisions require precise knowledge of the causal effects of such decisions on outcomes, a task that becomes increasingly complex in dynamic business environments. We propose an idea of instrumenting while experimenting, whereby researchers can create their own instruments by injecting small, random variations directly into the decision-making process and then use such variations to obtain causal estimates of the impact of varying business decisions at scale without disrup...
• Online Learning with Sample Selection Bias AI翻译
  作者：Singhvi, Divya; Singhvi, Somya
  作者单位：New York University; University of Southern California
  摘要：We consider the problem of personalized recommendations on online platforms, where user preferences are unknown, and users interact with the platform through a series of sequential decisions (such as clicking to watch on video platforms or clicking to donate on donation platforms). The platform aims to maximize the final outcome (e.g., viewing duration on video platforms or donations on donation platforms). However, the platform only observes the final outcome for users who complete the first ...
• Better Regularization for Sequential Decision Spaces: Fast Convergence Rates for Nash, Correlated, and Team Equilibria AI翻译
  作者：Farina, Gabriele; Kroer, Christian; Sandholm, Tuomas
  作者单位：Columbia University; Carnegie Mellon University
  摘要：We study the application of iterative first-order methods to the problem of computing equilibria of large-scale extensive-form games. First-order methods must typically be instantiated with a regularizer that serves as a distance-generating function (DGF) for the decision sets of the players. In this paper, we introduce a new weighted entropy-based distance-generating function. We show that this function is equivalent to a particular set of new weights for the dilated entropy distance-generati...
• Change-Point Detection in Dynamic Networks with Missing Links AI翻译
  作者：Enikeeva, Farida; Klopp, Olga
  作者单位：Universite de Poitiers
  摘要：Structural changes occur in dynamic networks quite frequently and their detection is an important question in many situations, such as fraud detection or cybersecurity. Real-life networks are often incompletely observed because of individual nonresponse or network size. In the present paper, we consider the problem of change-point detection at a temporal sequence of partially observed networks. The goal is to test whether there is a change in the network parameters. Our approach is based on th...
• A Robust Optimization Approach to Network Control Using Local Information Exchange AI翻译
  作者：Darivianakis, Georgios; Georghiou, Angelos; Shafiee, Soroosh; Lygeros, John
  作者单位：University of Cyprus; Cornell University; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：Designing policies for a network of agents is typically done by formulating an optimization problem where each agent has access to state measurements of all the other agents in the network. Such policy designs with centralized information exchange result in optimization problems that are typically hard to solve, require establishing substantial communication links, and do not promote privacy since all information is shared among the agents. Designing policies based on arbitrary communication s...
• Regular Variable Returns to Scale Production Frontier and Efficiency Measurement AI翻译
  作者：Li, Sung Ko; Tsang, Chun Kei; Lee, Shu Kam; He, Xinju
  作者单位：Hong Kong Shue Yan University; Hefei University of Technology; Hong Kong Baptist University; Guangxi University; Guangxi University
  摘要：The most frequently used empirical production frontier in data envelopment analysis, the variable returns to scale frontier, has a convex technology set and displays a special structure in economics, called the regular variable returns to scale in this paper; the production technology exhibits increasing returns to scale at the beginning of the production process followed by constant returns to scale and decreasing returns to scale. When the assumption of convexity is relaxed, modeling regular...
• Robustifying Conditional Portfolio Decisions via Optimal Transport AI翻译
  作者：Nguyen, Viet Anh; Zhang, Fan; Wang, Shanshan; Blanchet, Jose; Delage, Erick; Ye, Yinyu
  作者单位：Chinese University of Hong Kong; Stanford University; Beihang University; Universite de Montreal; HEC Montreal
  摘要：We propose a data-driven portfolio selection model that integrates side information, conditional estimation, and robustness using the framework of distributionally robust optimization. Conditioning on the observed side information, the portfolio manager solves an allocation problem that minimizes the worst-case conditional risk-return tradeoff, subject to all possible perturbations of the covariate-return probability distribution in an optimal transport ambiguity set. Despite the nonlinearity ...