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Found 7 papers, 3 papers with code
Upper bounds for Model-Free Row-Sparse Principal Component Analysis
We propose a new framework that finds out upper (dual) bounds for the sparse PCA within polynomial time via solving a convex integer program (IP).
Do algorithms and barriers for sparse principal component analysis extend to other structured settings?
We study a principal component analysis problem under the spiked Wishart model in which the structure in the signal is captured by a class of union-of-subspace models.
Individualized Treatment Allocation in Sequential Network Games
Focusing on sequential decision games of interacting agents, this paper develops a method to obtain optimal treatment assignment rules that maximize a social welfare criterion by evaluating stationary distributions of outcomes.
Only Train Once: A One-Shot Neural Network Training And Pruning Framework
Structured pruning is a commonly used technique in deploying deep neural networks (DNNs) onto resource-constrained devices.
A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization
Optimizing with group sparsity is significant in enhancing model interpretability in machining learning applications, e. g., feature selection, compressed sensing and model compression.
Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network
How to allocate vaccines over heterogeneous individuals is one of the important policy decisions in pandemic times.
Orthant Based Proximal Stochastic Gradient Method for $\ell_1$-Regularized Optimization
Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression.
