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Found 4 papers, 1 papers with code
FastPart: Over-Parameterized Stochastic Gradient Descent for Sparse optimisation on Measures
This paper presents a novel algorithm that leverages Stochastic Gradient Descent strategies in conjunction with Random Features to augment the scalability of Conic Particle Gradient Descent (CPGD) specifically tailored for solving sparse optimisation problems on measures.
Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) data
A quantitative prediction objective can be enriched by qualitative data interpretation, for instance by locating the most influential features.
SuperMix: Sparse Regularization for Mixtures
This paper investigates the statistical estimation of a discrete mixing measure $\mu$0 involved in a kernel mixture model.
Classification with the nearest neighbor rule in general finite dimensional spaces: necessary and sufficient conditions
Given an $n$-sample of random vectors $(X_i, Y_i)_{1 \leq i \leq n}$ whose joint law is unknown, the long-standing problem of supervised classification aims to \textit{optimally} predict the label $Y$ of a given a new observation $X$.
