3期

最新文章

• The Social Divide of Social Distancing: Shelter-in-Place Behavior in Santiago During the Covid-19 Pandemic AI翻译
  作者：Carranza, Aldo; Goic, Marcel; Lara, Eduardo; Olivares, Marcelo; Weintraub, Gabriel Y.; Covarrubia, Julio; Escobedo, Cristian; Jara, Natalia; Basso, Leonardo J.
  作者单位：Stanford University; Universidad de Chile; Universidad de Chile; Stanford University; Universidad de Chile; Universidad de Chile
  摘要：Voluntary shelter-in-place directives and lockdowns are the main nonpharmaceutical interventions that governments around the globe have used to contain the Covid-19 pandemic. In this paper, we study the impact of such interventions in the capital of a developing country, Santiago, Chile, that exhibits large socioeconomic inequality. A distinctive feature of our study is that we use granular geolocated mobile phone data to construct mobility measures that capture (1) shelter-in-place behavior a...
• Scaled PCA: A New Approach to Dimension Reduction AI翻译
  作者：Huang, Dashan; Jiang, Fuwei; Li, Kunpeng; Tong, Guoshi; Zhou, Guofu
  作者单位：Singapore Management University; Central University of Finance & Economics; Capital University of Economics & Business; Fudan University; Washington University (WUSTL)
  摘要：This paper proposes a novel supervised learning technique for forecasting: scaled principal component analysis (sPCA). The sPCA improves the traditional principal component analysis (PCA) by scaling each predictor with its predictive slope on the target to be forecasted. Unlike the PCA that maximizes the common variation of the predictors, the sPCA assigns more weight to those predictors with stronger forecasting power. In a general factor framework, we show that, under some appropriate condit...
• Data Pooling in Stochastic Optimization AI翻译
  作者：Gupta, Vishal; Kallus, Nathan
  作者单位：Cornell University; Cornell University
  摘要：Managing large-scale systems often involves simultaneously solving thousands of unrelated stochastic optimization problems, each with limited data. Intuition suggests that one can decouple these unrelated problems and solve them separately without loss of generality. We propose a novel data-pooling algorithm called Shrunken-SAA that disproves this intuition. In particular, we prove that combining data across problems can outperform decoupling, even when there is no a priori structure linking t...
• Inventory Balancing with Online Learning AI翻译
  作者：Cheung, Wang Chi; Ma, Will; Simchi-Levi, David; Wang, Xinshang
  作者单位：National University of Singapore; Columbia University; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Shanghai Jiao Tong University
  摘要：We study a general problem of allocating limited resources to heterogeneous customers over time under model uncertainty. Each type of customer can be serviced using different actions, each of which stochastically consumes some combination of resources and returns different rewards for the resources consumed. We consider a general model in which the resource consumption distribution associated with each customer type-action combination is not known but is consistent and can be learned over time...
• Adaptive Clinical Trial Designs with Surrogates: When Should We Bother? AI翻译
  作者：Anderer, Arielle; Bastani, Hamsa; Silberholz, John
  作者单位：University of Pennsylvania; University of Michigan System; University of Michigan
  摘要：The success of a new drug is assessed within a clinical trial using a primary endpoint, which is typically the true outcome of interest-for example, overall survival. However, regulators sometimes approve drugs using a surrogate outcome-an intermediate indicator that is faster or easier to measure than the true outcome of interest-for example, progression-free survival-as the primary endpoint when there is demonstrable medical need. Although using a surrogate outcome (instead of the true outco...
• Product Choice with Large Assortments: A Scalable Deep-Learning Model AI翻译
  作者：Gabel, Sebastian; Timoshenko, Artem
  作者单位：Humboldt University of Berlin; Northwestern University
  摘要：Personalized marketing in retail requires a model to predict how different marketing actions affect product choices by individual customers. Large retailers often handle millions of transactions daily, involving thousands of products in hundreds of categories. Product choice models thus need to scale to large product assortments and customer bases, without extensive product attribute information. To address these challenges, we propose a custom deep neural network model. The model incorporates...
• Interpretable Optimal Stopping AI翻译
  作者：Ciocan, Dragos Florin; Misic, Velibor V.
  作者单位：INSEAD Business School; University of California System; University of California Los Angeles
  摘要：Optimal stopping is the problem of deciding when to stop a stochastic system to obtain the greatest reward, arising in numerous application areas such as finance, healthcare, and marketing. State-of-the-art methods for high-dimensional optimal stopping involve approximating the value function or the continuation value and then using that approximation within a greedy policy. Although such policies can perform very well, they are generally not guaranteed to be interpretable; that is, a decision...
• Meta Dynamic Pricing: Transfer Learning Across Experiments AI翻译
  作者：Bastani, Hamsa; Simchi-Levi, David; Zhu, Ruihao
  作者单位：University of Pennsylvania; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Purdue University System; Purdue University
  摘要：We study the problem of learning shared structure across a sequence of dynamic pricing experiments for related products. We consider a practical formulation in which the unknown demand parameters for each product come from an unknown distribution (prior) that is shared across products. We then propose a meta dynamic pricing algorithm that learns this prior online while solving a sequence of Thompson sampling pricing experiments (each with horizon T) for N different products. Our algorithm addr...