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作者：Zheng, Yufeng; Zheng, Zeyu; Zhu, Tingyu
作者单位：University of Toronto; University of California System; University of California Berkeley
摘要：We propose a framework that integrates classic Monte Carlo simulators and Wasserstein generative adversarial networks to model, estimate, and simulate a broad class of arrival processes with general nonstationary and multidimensional random arrival rates. Classic Monte Carlo simulators have advantages in capturing the interpretable physics of a stochastic object, whereas neural network-based simulators have advantages in capturing less interpretable complicated dependence within a high-dimensi...
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作者：Wang, Yining
作者单位：University of Texas System; University of Texas Dallas
摘要：In this paper, we study the nonstationary stochastic optimization problem with bandit feedback and dynamic regret measures. The seminal work of Besbes et al. (2015) shows that, when aggregated function changes are known a priori, a simple restarting algorithm attains the optimal dynamic regret. In this work, we design a stochastic optimi-zation algorithm with fixed step sizes, which, combined with the multiscale sampling framework in existing research, achieves the optimal dynamic regret in no...
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作者：Bichler, Martin; Fichtl, Max; Oberlechner, Matthias
作者单位：Technical University of Munich
摘要：Auctions are modeled as Bayesian games with continuous type and action spaces. Determining equilibria in auction games is computationally hard in general, and no exact solution theory is known. We introduce an algorithmic framework in which we discretize type and action space and then learn distributional strategies via online optimization algorithms. One advantage of distributional strategies is that we do not have to make any assumptions on the shape of the bid function. Besides, the expecte...
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作者：Chen, Xin; He, Niao; Hu, Yifan; Ye, Zikun
作者单位：University System of Georgia; Georgia Institute of Technology; Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Washington; University of Washington Seattle
摘要：We study a class of stochastic nonconvex optimization in the form of min(x is an element of X) F(x) := E-xi[f (phi(x, xi))], that is, F is a composition of a convex function f and a random function phi. Leveraging an (implicit) convex reformulation via a variable transformation u = E[phi(x, xi)], we develop stochastic gradient-based algorithms and establish their sample and gradient complexities for achieving an epsilon-global optimal solution. Interestingly, our proposed Mirror Stochastic Gra...
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作者：Cominetti, Roberto; Scarsini, Marco; Schroder, Marc; Stier-Mosesd, Nicolas E.
作者单位：Universidad Adolfo Ibanez; Luiss Guido Carli University; Maastricht University
摘要：We consider an atomic congestion game in which each player i participates in the game with an exogenous and known probability p(i) is an element of (0, 1], independently of everybody else, or stays out and incurs no cost. We compute the parameterized price of anarchy to characterize the impact of demand uncertainty on the efficiency of selfish behavior, considering two different notions of a social planner. A prophet planner knows the realization of the random participation in the game; the or...
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作者：Balcan, Maria-Florina; Sandholm, Tuomas; Vitercik, Ellen
作者单位：Carnegie Mellon University; Stanford University; Stanford University
摘要：We study multi-item profit maximization when there is an underlying distribution over buyers' values. In practice, a full description of the distribution is typically unavailable, so we study the setting where the mechanism designer only has samples from the distribution. If the designer uses the samples to optimize over a complex mechanism class- such as the set of all multi-item, multibuyer mechanisms-a mechanism may have high average profit over the samples, but low expected profit. This ra...
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作者：Bai, Hao; Bensoussan, Alain; Briest, Gordon; Chevalier-Roignant, Benoi
作者单位：University of Manchester; University of Texas System; University of Texas Dallas; Otto von Guericke University; emlyon business school
摘要：Many cities face challenges in financing their infrastructure. If a decision maker cannot capture all the benefits of its investment, there is a risk of underinvestment. Hong Kong's transit operator designed a scheme in which it not only receives fare revenues, but also participates in a property management business, exploiting the positive externalities of public transport on nearby property prices. We develop a stochastic Stackelberg game of timing to explore the rationale of this scheme. Th...
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作者：Hu, Jie; Chen, Zhi; Wang, Shuming
作者单位：Beijing Jiaotong University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese University of Hong Kong
摘要：We study the (un)capacitated multiperiod hub location problem with uncertain periodic demands. With a distributionally robust approach that considers time series, we build a model driven by budgets on periodic costs. In particular, we construct a nested ambiguity set that characterizes uncertain periodic demands via a general multivariate time-series model, and to ensure stable periodic costs, we propose to constrain each expected periodic cost within a budget whereas optimizing the robustness...
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作者：Gotoh, Jun-ya; Kim, Michael Jong; Lim, Andrew E. B.
作者单位：Chuo University; University of British Columbia; National University of Singapore; National University of Singapore
摘要：Whereas solutions of distributionally robust optimization (DRO) problems can sometimes have a higher out-of-sample expected reward than the sample average approximation (SAA), there is no guarantee. In this paper, we introduce a class of distributionally optimistic optimization (DOO) models and show that it is always possible to beat SAA out-of-sample if we consider not just worst case (DRO) models but also best case (DOO) ones. We also show, however, that this comes at a cost: optimistic solu...
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作者：Cheng, Chun; Sim, Melvyn; Zhao, Yue
作者单位：Dalian University of Technology; National University of Singapore; National University of Singapore
摘要：We investigate how crowdsourced delivery platforms with both contracted and ad hoc couriers can effectively manage their workforce to meet delivery demands amidst uncertainties. Our objective is to minimize the hiring costs of contracted couriers and the crowdsourcing costs of ad hoc couriers, while considering the uncertain availability and behavior of the latter. Because of the complication of calibrating these uncertainties through data-driven approaches, we instead introduce a basic reduce...
