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作者：Gupta, Varun; Radovanovic, Ana
作者单位：University of Chicago; Alphabet Inc.; Google Incorporated
摘要：Bin packing is an algorithmic problem that arises in diverse applications such as remnant inventory systems, shipping logistics, and appointment scheduling. In its simplest variant, a sequence of T items (e.g., orders for raw material, packages for delivery) is revealed one at a time, and each item must be packed on arrival in an available bin (e.g., remnant pieces of raw material in inventory, shipping containers). The sizes of items are independent and identically distributed (i.i.d.) sample...
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作者：Mintz, Yonatan; Aswani, Anil; Kaminsky, Philip; Flowers, Elena; Fukuoka, Yoshimi
作者单位：University System of Georgia; Georgia Institute of Technology; University of California System; University of California Berkeley; University of California System; University of California San Francisco; University of California System; University of California San Francisco
摘要：Many settings involve sequential decision making where a set of actions can be chosen at each time step, each action provides a stochastic reward, and the distribution for the reward provided by each action is initially unknown. However, frequent selection of a specific action may reduce the expected reward for that action, whereas abstaining from choosing an action may cause its expected reward to increase. Such nonstationary phenomena are observed in many real-world settings such as personal...
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作者：Han, Weidong; Powell, Warren B.
作者单位：Princeton University
摘要：We consider an optimal learning problem where we are trying to learn a function that is nonlinear in unknown parameters in an online setting. We formulate the problem as a dynamic program, provide the optimality condition using Bellman's equation, and propose a multiperiod lookahead policy to overcome the nonconcavity in the value of information. We adopt a sampled belief model, which we refer to as a discrete prior. For an infinite-horizon problem with discounted cumulative rewards, we prove ...
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作者：Glynn, Peter W.; Fan, Lin; Fu, Michael C.; Hu, Jian-Qiang; Peng, Yijie
作者单位：Stanford University; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park; Fudan University; Peking University
摘要：We provide a simple proof of the central limit theorem (CLT) for estimated functions at estimated points. Such estimators arise in a number of different simulationbased computational settings. We illustrate the methodology via applications to quantile estimation and related sensitivity analysis, as well as to computation of conditional valueat-risk.
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作者：Ebert, Sebastian
作者单位：Frankfurt School Finance & Management; Tilburg University
摘要：This article gives a comprehensive treatment of preferences regarding time risk-the risk of something happening sooner or later-within the expected discounted utility model. We characterize the signs of the discount function's derivatives of all orders and show how these signs are decisive for time risk preferences. We introduce the notions of prudent and temperate discounting and illustrate their importance for economic behavior. Several applications in which an important event is only a matt...
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作者：Shapiro, Alexander; Xin, Linwei
作者单位：University System of Georgia; Georgia Institute of Technology; University of Chicago
摘要：In this paper, we investigate optimal policies of distributionally robust (risk averse) inventory models. We demonstrate that if the respective risk measures are not strictly monotone, then there may exist infinitely many optimal policies that are not basestock and not time consistent. This is in a sharp contrast with the risk neutral formulation of the inventory model where all optimal policies are time consistent. This also extends previous studies of time inconsistency in the robust setting.
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作者：Modaresi, Sajad; Saure, Denis; Vielma, Juan Pablo
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Universidad de Chile; Massachusetts Institute of Technology (MIT)
摘要：We study dynamic decision making under uncertainty when, at each period, a decision maker implements a solution to a combinatorial optimization problem. The objective coefficient vectors of said problem, which are unobserved before implementation, vary from period to period. These vectors, however, are known to be random draws from an initially unknown distribution with known range. By implementing different solutions, the decision maker extracts information about the underlying distribution b...
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作者：Hazimeh, Hussein; Mazumder, Rahul
作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
摘要：The L-0-regularized least squares problem (a.k.a. best subsets) is central to sparse statistical learning and has attracted significant attention across the wider statistics, machine learning, and optimization communities. Recent work has shown that modern mixed integer optimization (MIO) solvers can be used to address small to moderate instances of this problem. In spite of the usefulness of L-0-based estimators and generic MIO solvers, there is a steep computational price to pay when compare...
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作者：Misic, Velibor V.
作者单位：University of California System; University of California Los Angeles
摘要：Tree ensemble models such as random forests and boosted trees are among the most widely used and practically successful predictive models in applied machine learning and business analytics. Although such models have been used to make predictions based on exogenous, uncontrollable independent variables, they are increasingly being used to make predictions where the independent variables are controllable and are also decision variables. In this paper, we study the problem of tree ensemble optimi...
