60 citations · 197 across the 15 of their papers we have counts for
16 papers
msPCA: An R Package for Sparse PCA with Multiple Components
Ryan Cory-Wright, Jean Pauphilet
We present msPCA: an open-source R package for sparse principal component analysis with multiple components. It implements an alternating maximization algorithm to generate a set o…
Improved Approximation Algorithms for Orthogonally Constrained Problems Using Semidefinite Optimization
Ryan Cory-Wright, Jean Pauphilet
Building on the blueprint from Goemans and Williamson (1995) for the Max-Cut problem, we construct a polynomial-time approximation algorithm for orthogonally constrained quadratic…
Adaptive Optimization for Prediction with Missing Data
Dimitris Bertsimas, Arthur Delarue, Jean Pauphilet
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 com…
Patient Outcome Predictions Improve Operations at a Large Hospital Network
Liangyuan Na, Kimberly Villalobos Carballo, Jean Pauphilet +8
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…
Disjunctive Branch-and-Bound for Certifiably Optimal Low-Rank Matrix Completion
Dimitris Bertsimas, Ryan Cory-Wright, Sean Lo +1
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…
A Stochastic Benders Decomposition Scheme for Large-Scale Stochastic Network Design
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet +1
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 w…
Sparse PCA With Multiple Components
Ryan Cory-Wright, Jean Pauphilet
Sparse Principal Component Analysis (sPCA) is a cardinal technique for obtaining combinations of features, or principal components (PCs), that explain the variance of high-dimensio…
A new perspective on low-rank optimization
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet
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…
Simple Imputation Rules for Prediction with Missing Data: Contrasting Theoretical Guarantees with Empirical Performance
Dimitris Bertsimas, Arthur Delarue, Jean Pauphilet
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 est…
Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet
We propose a framework for modeling and solving low-rank optimization problems to certifiable optimality. We introduce symmetric projection matrices that satisfy , the matri…
From predictions to prescriptions: A data-driven response to COVID-19
Dimitris Bertsimas, Léonard Boussioux, Ryan Cory Wright +18
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a d…
Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet
Sparse principal component analysis (PCA) is a popular dimensionality reduction technique for obtaining principal components which are linear combinations of a small subset of the…
A unified approach to mixed-integer optimization problems with logical constraints
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet
We propose a unified framework to address a family of classical mixed-integer optimization problems with logically constrained decision variables, including network design, facilit…
Certifiably Optimal Sparse Inverse Covariance Estimation
Dimitris Bertsimas, Jourdain Lamperski, Jean Pauphilet
We consider the maximum likelihood estimation of sparse inverse covariance matrices. We demonstrate that current heuristic approaches primarily encourage robustness, instead of the…
Sparse Regression: Scalable algorithms and empirical performance
Dimitris Bertsimas, Jean Pauphilet, Bart Van Parys
In this paper, we review state-of-the-art methods for feature selection in statistics with an application-oriented eye. Indeed, sparsity is a valuable property and the profusion of…
Sparse Classification: a scalable discrete optimization perspective
Dimitris Bertsimas, Jean Pauphilet, Bart Van Parys
We formulate the sparse classification problem of samples with features as a binary convex optimization problem and propose a cutting-plane algorithm to solve it exactly. F…