6期

最新文章

• Weighted Scoring Rules and Convex Risk Measures AI翻译
  作者：Smith, Zachary J.; Bickel, J. Eric
  作者单位：University of Texas System; University of Texas Austin
  摘要：This paper establishes a new relationship between proper scoring rules and convex risk measures. Specifically, we demonstrate that the entropy function associated with any weighted scoring rule is equal to the maximum value of an optimization problem where an investor maximizes a concave certainty equivalent (the negation of a convex risk measure). Using this connection, we construct two classes of proper weighted scoring rules with associated entropy functions based on phi-divergences. These ...
• A Probabilistic Approach to Growth Networks AI翻译
  作者：Jelenkovic, Predrag; Kondev, Jane; Mohapatra, Lishibanya; Momcilovic, Petar
  作者单位：Columbia University; Brandeis University; Rochester Institute of Technology; Texas A&M University System; Texas A&M University College Station
  摘要：Widely used closed product-form networks have emerged recently as a primary model of stochastic growth of subcellular structures, for example, cellular filaments. The baseline bio-molecular model is equivalent to a single-class closed queueing network, consisting of single-server and infinite-server queues. Although this model admits a seemingly tractable product-form solution, explicit analytical characterization of its partition function is difficult due to the large-scale nature of bio-mole...
• Shortfall Risk Models When Information on Loss Function Is Incomplete AI翻译
  作者：Delage, Erick; Guo, Shaoyan; Xu, Huifu
  作者单位：Universite de Montreal; HEC Montreal; Universite de Montreal; HEC Montreal; Dalian University of Technology; Chinese University of Hong Kong
  摘要：The utility-based shortfall risk (SR) measure effectively captures a decisionmaker's risk attitude on tail losses by an increasing convex loss function. In this paper, we consider a situation where the decision maker's risk attitude toward tail losses is ambiguous and introduce a robust version of SR, which mitigates the risk arising from such ambiguity. Specifically, we use some available partial information or subjective judgement to construct a set of utility-based loss functions and define...
• Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces AI翻译
  作者：Eckman, David J.; Plumlee, Matthew; Nelson, Barry L.
  作者单位：Texas A&M University System; Texas A&M University College Station; Northwestern University
  摘要：When working with models that allow for many candidate solutions, simulation practitioners can benefit fromscreening out unacceptable solutions in a statistically controlled way. However, for large solution spaces, estimating the performance of all solutions through simulation can prove impractical. We propose a statistical framework for screening solutions even when only a relatively small subset of them is simulated. Our framework derives its superiority over exhaustive screening approaches ...
• Actor-Critic-Like Stochastic Adaptive Search for Continuous Simulation Optimization AI翻译
  作者：Zhang, Qi; Hu, Jiaqiao
  作者单位：State University of New York (SUNY) System; Stony Brook University
  摘要：We propose a random search method for solving a class of simulation optimization problems with Lipschitz continuity properties. The algorithm samples candidate solutions from a parameterized probability distribution over the solution space and estimates the performance of the sampled points through an asynchronous learning procedure based on the so-called shrinking ball method. A distinctive feature of the algorithm is that it fully retains the previous simulation information and incorporates ...
• High-Dimensional Learning Under Approximate Sparsity with Applications to Nonsmooth Estimation and Regularized Neural Networks AI翻译
  作者：Liu, Hongcheng; Ye, Yinyu; Lee, Hung Yi
  作者单位：State University System of Florida; University of Florida; Stanford University
  摘要：High-dimensional statistical learning (HDSL) has wide applications in data analysis, operations research, and decision making. Despite the availability of multiple theoretical frameworks, most existing HDSL schemes stipulate the following two conditions: (a) the sparsity and (b) restricted strong convexity (RSC). This paper generalizes both conditions via the use of the folded concave penalty (FCP). More specifically, we consider an M-estimation problem where (i) (conventional) sparsity is rel...
• Bayesian Optimization Allowing for Common Random Numbers AI翻译
  作者：Pearce, Michael Arthur Leopold; Poloczek, Matthias; Branke, Juergen
  作者单位：University of Warwick; Amazon.com; University of Warwick
  摘要：Bayesian optimization is a powerful tool for expensive stochastic black-box optimization problems, such as simulation-based optimization or machine learning hyperparameter tuning. Many stochastic objective functions implicitly require a random number seed as input. By explicitly reusing a seed, a user can exploit common random numbers, comparing two or more inputs under the same randomly generated scenario, such as a common customer stream in a job shop problem or the same random partition of ...
• Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints AI翻译
  作者：Bertsimas, Dimitris; Cory-Wright, Ryan; Pauphilet, Jean
  作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of London; London Business School
  摘要：We propose a framework for modeling and solving low-rank optimization problems to certifiable optimality. We introduce symmetric projection matrices that satisfy Y-2 = Y, the matrix analog of binary variables that satisfy z(2) = z, to model rank constraints. By leveraging regularization and strong duality, we prove that this modeling paradigm yields convex optimization problems over the nonconvex set of orthogonal projection matrices. Furthermore, we design outer-approximation algorithms to so...
• Building a Location-Based Set of Social Media Users AI翻译
  作者：Marks, Christopher Edward; Zaman, Tauhid
  作者单位：Massachusetts Institute of Technology (MIT); The Charles Stark Draper Laboratory, Inc.; Yale University
  摘要：In many instances, one may want to gain situational awareness in an environment by monitoring the content of local social media users. Often the challenge is how to build a set of users from a target location. Here, we introduce a method for building such a set of users by using an expand-classify approach, which begins with a small set of seed users from the target location and then iteratively collects their neighbors and classifies their locations. We perform this classification using maxim...
• ALSO-X and ALSO-X1: Better Convex Approximations for Chance Constrained Programs AI翻译
  作者：Jiang, Nan; Xie, Weijun
  作者单位：Virginia Polytechnic Institute & State University
  摘要：In a chance constrained program (CCP), decision makers seek the best decision whose probability of violating the uncertainty constraints is within the prespecified risk level. As a CCP is often nonconvex and is difficult to solve to optimality, much effort has been devoted to developing convex inner approximations for a CCP, among which the conditional value-at-risk (CVaR) has been known to be the best for more than a decade. This paper studies and generalizes the ALSO-X, originally proposed b...