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Found 7 papers, 1 papers with code
A Provably Accurate Randomized Sampling Algorithm for Logistic Regression
When the number of observations greatly exceeds the number of predictor variables, we present a simple, randomized sampling-based algorithm for logistic regression problem that guarantees high-quality approximations to both the estimated probabilities and the overall discrepancy of the model.
Deep Learning with Physics Priors as Generalized Regularizers
In various scientific and engineering applications, there is typically an approximate model of the underlying complex system, even though it contains both aleatoric and epistemic uncertainties.
Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs
Linear programming (LP) is used in many machine learning applications, such as $\ell_1$-regularized SVMs, basis pursuit, nonnegative matrix factorization, etc.
Approximation Algorithms for Sparse Principal Component Analysis
To improve the interpretability of PCA, various approaches to obtain sparse principal direction loadings have been proposed, which are termed Sparse Principal Component Analysis (SPCA).
Randomized Iterative Algorithms for Fisher Discriminant Analysis
When the number of predictor variables greatly exceeds the number of observations, one of the alternatives for conventional FDA is regularized Fisher discriminant analysis (RFDA).
An Iterative, Sketching-based Framework for Ridge Regression
Ridge regression is a variant of regularized least squares regression that is particularly suitable in settings where the number of predictor variables greatly exceeds the number of observations.
Structural Conditions for Projection-Cost Preservation via Randomized Matrix Multiplication
Projection-cost preservation is a low-rank approximation guarantee which ensures that the cost of any rank-$k$ projection can be preserved using a smaller sketch of the original data matrix.
