6 papers · 1 filter
Efficient Cross-Validation for Sparse Linear Regression
Ryan Cory-Wright, Andrés Gómez
Given a high-dimensional covariate matrix and a response vector, ridge-regularized sparse linear regression selects a subset of features that explains the relationship between cova…
Compact Lifted Relaxations for Low-Rank Optimization
Ryan Cory-Wright, Jean Pauphilet
We develop tractable convex relaxations for rank-constrained quadratic optimization problems over matrices, a setting for which tractable relaxations are typically onl…
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…
Pricing Discrete and Nonlinear Markets With Semidefinite Relaxations
Cheng Guo, Lauren Henderson, Ryan Cory-Wright +1
Nonconvexities in markets with discrete decisions and nonlinear constraints make efficient pricing challenging, often necessitating subsidies. A prime example is the unit commitmen…
Stability Regularized Cross-Validation
Ryan Cory-Wright, Andrés Gómez
We revisit the problem of ensuring strong test set performance via cross-validation, and propose a nested k-fold cross-validation scheme that selects hyperparameters by minimizing…
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…