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08/18/2023
AI Hilbert: From Data and Background Knowledge to Automated Scientific Discovery
The discovery of scientific formulae that parsimoniously explain natural...
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06/26/2023
Gain Confidence, Reduce Disappointment: A New Approach to Cross-Validation for Sparse Regression
Ridge regularized sparse regression involves selecting a subset of featu...
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05/20/2023
Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions
Low-rank matrix completion consists of computing a matrix of minimal com...
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09/29/2022
Sparse PCA With Multiple Components
Sparse Principal Component Analysis is a cardinal technique for obtainin...
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09/26/2021
Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach
We study the Sparse Plus Low Rank decomposition problem (SLR), which is ...
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05/12/2021
A new perspective on low-rank optimization
A key question in many low-rank problems throughout optimization, machin...
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09/22/2020
Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints
We propose a framework for modeling and solving low-rank optimization pr...
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06/30/2020
From predictions to prescriptions: A data-driven response to COVID-19
The COVID-19 pandemic has created unprecedented challenges worldwide. St...
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05/11/2020
Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
Sparse principal component analysis (PCA) is a popular dimensionality re...
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10/08/2019
On Polyhedral and Second-Order-Cone Decompositions of Semidefinite Optimization Problems
We study a cutting-plane method for semidefinite optimization problems (...
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07/03/2019
