6期

最新文章

• Regularized Aggregation of One-Off Probability Predictions AI翻译
  作者：Satopaa, Ville A.
  作者单位：INSEAD Business School
  摘要：Forecasters predicting the chances of a future event may disagree because of differing evidence or noise. To harness the collective evidence of the crowd, we propose a Bayesian aggregator that is regularized by analyzing the forecasters' disagreement and ascribing overdispersion to noise. Our aggregator requires no user intervention and can be computed efficiently even for a large number of predictions. To illustrate, we evaluate our aggregator on subjective probability predictions collected d...
• Data Science for Motion and Time Analysis with Modern Motion Sensor Data AI翻译
  作者：Park, Chiwoo; Do Noh, Sang; Srivastava, Anuj
  作者单位：State University System of Florida; Florida State University; Sungkyunkwan University (SKKU); State University System of Florida; Florida State University
  摘要：The analysis of motion and time has become significant in operations research, especially for analyzing work performance in manufacturing and service operations in the development of lean manufacturing and smart factory. This paper develops a framework for data-driven analysis of work motions and studies their correlations to work speeds or execution rates, using data collected from modern motion sensors. Past efforts primarily relied on manual steps involving time-consuming stop-watching, vid...
• Scheduling Parallel-Task Jobs Subject to Packing and Placement Constraints AI翻译
  作者：Shafiee, Mehrnoosh; Ghaderi, Javad
  作者单位：Columbia University
  摘要：Motivated bymodern parallel computing applications, we consider the problem of scheduling parallel-task jobs with heterogeneous resource requirements in a cluster of machines. Each job consists of a set of tasks that can be processed in parallel; however, the job is considered completed only when all its tasks finish their processing, which we refer to as the synchronization constraint. Furthermore, assignment of tasks to machines is subject to placement constraints, that is, each task can be ...
• 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 ...
• 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...
• 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 ...
• 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...
• 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...
• Naive Learning Through Probability Overmatching AI翻译
  作者：Arieli, Itai; Babichenko, Yakov; Mueller-Frank, Manuel
  作者单位：Technion Israel Institute of Technology; University of Navarra; IESE Business School
  摘要：We analyze boundedly rational updating in a repeated interaction network model with binary actions and binary states. Agents form beliefs according to discretized DeGroot updating and apply a decision rule that assigns a (mixed) action to each belief. We first show that under weak assumptions, random decision rules are sufficient to achieve agreement in finite time in any strongly connected network. Ourmain result establishes that naive learning can be achieved in any large strongly connected ...
• Iterative Collaborative Filtering for Sparse Matrix Estimation AI翻译
  作者：Borgs, Christian; Chayes, Jennifer T.; Shah, Devavrat; Yu, Christina Lee
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; University of California System; University of California Berkeley; Cornell University
  摘要：We consider sparse matrix estimation where the goal is to estimate an n-by-n matrix from noisy observations of a small subset of its entries. We analyze the estimation error of the popularly used collaborative filtering algorithm for the sparse regime. Specifically, we propose a novel iterative variant of the algorithm, adapted to handle the setting of sparse observations. We establish that as long as the number of entries observed at random scale logarithmically larger than linear in n, the e...