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Optimization on the Surface of the (Hyper)-Sphere
Thomson problem is a classical problem in physics to study how $n$ number of charged particles distribute themselves on the surface of a sphere of $k$ dimensions.
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.
Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy
Recent work has combined Stein’s method with reproducing kernel Hilbert space theory to develop nonparametric goodness-of-fit tests for un-normalized probability distributions.
Stochastic Gradient Descent for Relational Logistic Regression via Partial Network Crawls
Research in statistical relational learning has produced a number of methods for learning relational models from large-scale network data.
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.
