Sparse Methods for Machine Learning: Theory and Algorithms
Introduction to Machine Learning
User Behavior Modeling with Large-Scale Graph Analysis
Convex Relaxation and Estimation of High-Dimensional Matrices
Sigma point and particle approximations of stochastic differential equations in optimal filtering
Deep Belief Networks
On manifolds and autoencoders
Relational Learning as Collective Matrix Factorization
The Recommender Problem Revisited
Function Factorization Using Warped Gaussian Processes