-
作者：Kruse, Thomas; Strack, Philipp
作者单位：University of Wuppertal; Yale University
摘要：We analyze how to optimally engage in social distancing in order to minimize the spread of an infectious disease. We identify conditions under which any optimal policy is single peaked (i.e., first engages in increasingly more social distancing and subsequently decreases its intensity). We show that an optimal policy might substantially delay measures that decrease the transmission rate to create herd immunity and that engaging in social distancing suboptimally early can increase the number of...
-
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 ...
-
作者：Chen, Xi; Krishnamurthy, Akshay; Wang, Yining
作者单位：New York University; University of Texas System; University of Texas Dallas
摘要：We consider the dynamic assortment optimization problem under the multinomial logit model with unknown utility parameters. The main question investigated in this paper is model mis-specification under the e-contamination model, which is a fundamental model in robust statistics and machine learning. In particular, throughout a selling horizon of length T, we assume that customers make purchases according to a well-specified underlying multinomial logit choice model in a (1 - e)-fraction of the ...
-
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...
-
作者：Paccagnan, Dario; Gairing, Martin
作者单位：Imperial College London; University of Liverpool
摘要：In this work, we address the problem of minimizing social cost in atomic congestion games. For this problem, we present lower bounds on the approximation ratio achievable in polynomial time and demonstrate that efficiently computable taxes result in polynomial time algorithms matching such bounds. Perhaps surprisingly, these results show that indirect interventions, in the form of efficiently computed taxation mechanisms, yield the same performance achievable by the best polynomial time algori...
-
作者：Peng, Chun; Delage, Erick
作者单位：Beijing Jiaotong University; Universite de Montreal; HEC Montreal; Universite de Montreal; HEC Montreal
摘要：Optimization with stochastic dominance constraints has recently received an increasing amount of attention in the quantitative risk management literature. Instead of requiring that the probabilistic description of the uncertain parameters be exactly known, this paper presents a comprehensive study of a data-driven formulation of the distributionally robust second order stochastic dominance constrained problem (DRSSDCP) that hinges on using a type-1 Wasserstein ambiguity set. This formulation a...
-
作者：Gupta, Varun
作者单位：University of Chicago
摘要：In this paper, we prove the efficacy of a simple greedy algorithm for a finite horizon online resource allocation/matching problem when the corresponding static planning linear program (SPP) exhibits a nondegeneracy condition called the general position gap (GPG). The key intuition that we formalize is that the solution of the reward-maximizing SPP is the same as a feasibility linear program restricted to the optimal basic activities, and under GPG, this solution can be tracked with bounded re...
-
作者：Wang, Guangju; Zhang, Hailun; Zhang, Jiheng
作者单位：Shanghai Qi Zhi Institute; Shenzhen Research Institute of Big Data; Chinese University of Hong Kong; Hong Kong University of Science & Technology
摘要：Ride-hailing platforms, such as Uber, Lyft, and DiDi, coordinate supply and demand by matching passengers and drivers. The platform has to promptly dispatch drivers when receiving requests because, otherwise, passengers may lose patience and abandon the service by switching to alternative transportation methods. However, having fewer idle drivers results in a possible lengthy pickup time, which is a waste of system capacity and may cause passengers to cancel the service after they are matched....
-
作者：Zhang, Amy B. Z.; Gurvich, Itai
作者单位：Cornell University; Northwestern University
摘要：We introduce a framework to approximate Markov decision processes (MDPs) that stands on two pillars: (i) state aggregation, as the algorithmic infrastructure, and (ii) central-limit-theorem-type approximations, as the mathematical underpinning of optimality guarantees. The theory is grounded in recent work by Braverman et al. (2020) that relates the solution of the Bellman equation to that of a partial differential equation (PDE) where, in the spirit of the central limit theorem, the transitio...
-
作者：Chen, Xin; Li, Menglong
作者单位：University System of Georgia; Georgia Institute of Technology; City University of Hong Kong
摘要：We propose a new concept of S-convex functions (and its variant, semi strictly quasi -S-(SSQS)-convex functions) to study substitute structures in economics and operations models with continuous variables. We develop a host of fundamental properties and characterizations of S-convex functions, including various preservation properties, conjugate relationships with submodular and convex functions, and characterizations using Hessians. For a divisible market, we show that the utility function sa...
