Statistical Learning Theory: Models, Concepts, and Results

Statistical ranking and combinatorial Hodge theory

Statistical Topic Models for Multi-Label Document Classification

Strong Consistency of Prototype Based Clustering in Probabilistic Space

Structural Similarity and Distance in Learning

Structure Learning of Probabilistic Graphical Models: A Comprehensive Survey

Structured Sparse Principal Component Analysis

Structured variable selection in support vector machines

Structured Variable Selection with Sparsity-Inducing Norms

Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection

Subspace clustering of high-dimensional data: a predictive approach

Sufficient Component Analysis for Supervised Dimension Reduction

Super-Linear Convergence of Dual Augmented-Lagrangian Algorithm for Sparsity Regularized Estimation

Supervised classification for a family of Gaussian functional models

Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows

Supervised functional classification: A theoretical remark and some comparisons

Supervised Machine Learning with a Novel Kernel Density Estimator

Supervised Machine Learning with a Novel Pointwise Density Estimator

Supervised Topic Models

Supplementary material for Markov equivalence for ancestral graphs