Zobrazeno 1 - 10
of 94
pro vyhledávání: '"Pauphilet, Jean"'
When training predictive models on data with missing entries, the most widely used and versatile approach is a pipeline technique where we first impute missing entries and then compute predictions. In this paper, we view prediction with missing data
Externí odkaz:
http://arxiv.org/abs/2402.01543
Autor:
Na, Liangyuan, Carballo, Kimberly Villalobos, Pauphilet, Jean, Haddad-Sisakht, Ali, Kombert, Daniel, Boisjoli-Langlois, Melissa, Castiglione, Andrew, Khalifa, Maram, Hebbal, Pooja, Stein, Barry, Bertsimas, Dimitris
Problem definition: Access to accurate predictions of patients' outcomes can enhance medical staff's decision-making, which ultimately benefits all stakeholders in the hospitals. A large hospital network in the US has been collaborating with academic
Externí odkaz:
http://arxiv.org/abs/2305.15629
Low-rank matrix completion consists of computing a matrix of minimal complexity that recovers a given set of observations as accurately as possible. Unfortunately, existing methods for matrix completion are heuristics that, while highly scalable and
Externí odkaz:
http://arxiv.org/abs/2305.12292
Network design problems involve constructing edges in a transportation or supply chain network to minimize construction and daily operational costs. We study a stochastic version where operational costs are uncertain due to fluctuating demand and est
Externí odkaz:
http://arxiv.org/abs/2303.07695
Autor:
Cory-Wright, Ryan, Pauphilet, Jean
Sparse Principal Component Analysis (sPCA) is a cardinal technique for obtaining combinations of features, or principal components (PCs), that explain the variance of high-dimensional datasets in an interpretable manner. This involves solving a spars
Externí odkaz:
http://arxiv.org/abs/2209.14790
Autor:
Grand-Clément, Julien, Pauphilet, Jean
Many high-stake decisions follow an expert-in-loop structure in that a human operator receives recommendations from an algorithm but is the ultimate decision maker. Hence, the algorithm's recommendation may differ from the actual decision implemented
Externí odkaz:
http://arxiv.org/abs/2209.01874
Autor:
Pauphilet, Jean(Jean A.)
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 235-253).
In the next ten ye
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 235-253).
In the next ten ye
Externí odkaz:
https://hdl.handle.net/1721.1/127298
Autor:
Pauphilet, Jean
Problem definition: Mining for heterogeneous responses to an intervention is a crucial step for data-driven operations, for instance to personalize treatment or pricing. We investigate how to estimate price sensitivity from transaction-level data. In
Externí odkaz:
http://arxiv.org/abs/2106.11389
A key question in many low-rank problems throughout optimization, machine learning, and statistics is to characterize the convex hulls of simple low-rank sets and judiciously apply these convex hulls to obtain strong yet computationally tractable con
Externí odkaz:
http://arxiv.org/abs/2105.05947
Missing data is a common issue in real-world datasets. This paper studies the performance of impute-then-regress pipelines by contrasting theoretical and empirical evidence. We establish the asymptotic consistency of such pipelines for a broad family
Externí odkaz:
http://arxiv.org/abs/2104.03158