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作者：Birge, John R.; Chen, Hongfan (Kevin); Keskin, N. Bora; Ward, Amy
作者单位：University of Chicago; Chinese University of Hong Kong; Duke University
摘要：We consider a platform in which multiple sellers offer their products for sale over a time horizon of T periods. Each seller sets its own price. The platform collects a fraction of the sales revenue and provides price-setting incentives to the sellers to maximize its own revenue. The demand for each seller's product is a function of all sellers' prices and some customer features. Initially, neither the platform nor the sellers know the demand function, but they can learn about it through sales...
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作者：Lee, Ilbin
作者单位：University of Alberta
摘要：In recent applications of Markov decision processes (MDPs), it is common to estimate transition probabilities and rewards from transition data. In healthcare and some other applications, transition data are collected from a population of different entities, such as patients. Thus, one faces a modeling question of whether to estimate different models for subpopulations (e.g., divided by smoking status). For instance, there may be a subpopulation whose disease status progresses faster than other...
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作者：Zacharias, Christos; Liu, Nan; Begen, Mehmet A.
作者单位：University of Miami; Boston College; Western University (University of Western Ontario)
摘要：The simultaneous consideration of appointment day (interday scheduling) and time of day (intraday scheduling) in dynamic scheduling decisions is a theoretical and practical problem that has remained open. We introduce a novel dynamic programming framework that incorporates jointly these scheduling decisions in two timescales. Our model is designed with the intention of bridging the two streams of literature on interday and intraday scheduling and to leverage their latest theoretical developmen...
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作者：Hu, Feihong; Mitchell, Daniel; Tompaidis, Stathis
作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; Office of Financial Research; United States Department of the Treasury
摘要：We study networks of financial institutions where only aggregate information on liabilities is available. We introduce the robust liability network, that is, the network with the worst expected losses among all networks with the same aggregate liabilities and assets. We provide an algorithm to identify the robust liability network and, using aggregate data provided by bank holding companies to the Federal Reserve in form FR Y -9C, determine robust liability networks for U.S. banks under variou...
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作者：Huettner, Frank; Boyaci, Tamer; Akcay, Yalcin
作者单位：Sungkyunkwan University (SKKU); European School of Management & Technology; University of Melbourne
摘要：There is an error in one of the results of our paper [Huettner F, Boyaci T, Akcay Y (2019) Consumer choice under limited attention when alternatives have different information costs. Oper. Res. 67(3):671-699]. In this erratum, we point out the error and provide a correction based on Walker-Jones [(2023) Rational inattention with multiple attributes. J. Econom. Theory 212:105688]. Our key characterizations, insights, and numerical examples do not depend on this error and, hence, remain valid. T...
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作者：Yang, Bo; Nadarajah, Selvaprabu; Secomandi, Nicola
作者单位：Carnegie Mellon University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：We study merchant energy production modeled as a compound switching and timing option. The resulting Markov decision process is intractable. Least squares Monte Carlo combined with information relaxation and duality is a state-of-the-art reinforcement learning methodology to obtain operating policies and optimality gaps for related models. Pathwise optimization is a competing technique developed for optimal stopping settings, in which it typically provides superior results compared with this a...
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作者：Luo, Yuetian; Huang, Wen; Li, Xudong; Zhang, Anru
作者单位：University of Chicago; Xiamen University; Fudan University; Duke University
摘要：In this paper, we propose a recursive importance sketching algorithm for rank constrained least squares optimization (RISRO). The key step of RISRO is recursive importance sketching, a new sketching framework based on deterministically designed recursive projections, and it significantly differs from the randomized sketching in the literature. Several existing algorithms in the literature can be reinterpreted under this new sketching framework, and RISRO offers clear advantages over them. RISR...
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作者：Long, Zhenghua; Zhang, Hailun; Zhang, Jiheng; Zhang, Zhe George
作者单位：Nanjing University; Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong, Shenzhen; Hong Kong University of Science & Technology; Western Washington University; Simon Fraser University
摘要：We study the optimal control of a queueing model with a single customer class and heterogeneous server pools. The main objective is to strike a balance between the holding cost of the queue and the operating costs of the server pools. We introduce a target-allocation policy, which assigns higher priority to the queue or pools without enough customers for general cost functions. Although we can prove its asymptotic optimality, implementation requires solving a nonlinear optimization problem. Wh...
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作者：Javanmard, Adel; Mehrabi, Mohammad
作者单位：University of Southern California
摘要：Over the past few years, several adversarial training methods have been proposed to improve the robustness of machine learning models against adversarial perturbations in the input. Despite remarkable progress in this regard, adversarial training is often observed to drop the standard test accuracy. This phenomenon has intrigued the research community to investigate the potential tradeoff between standard accuracy (a.k.a generalization) and robust accuracy (a.k.a robust generalization) as two ...
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作者：Simchowitz, Max; Slivkins, Aleksandrs
作者单位：Massachusetts Institute of Technology (MIT)
摘要：How do you incentivize self-interested agents to explore when they prefer to exploit? We consider complex exploration problems, where each agent faces the same (but unknown) Markov decision process (MDP). In contrast with traditional formulations of reinforcement learning, agents control the choice of policies, whereas an algorithm can only issue recommendations. However, the algorithm controls the flow of information, and can incentivize the agents to explore via information asymmetry. We des...
