Learning Sets with Separating Kernels

Learning sparse gradients for variable selection and dimension reduction

Learning the Structure of Deep Sparse Graphical Models

Least Absolute Gradient Selector: Statistical Regression via Pseudo-Hard Thresholding

Least-Squares Independence Regression for Non-Linear Causal Inference under Non-Gaussian Noise

Likelihood-based semi-supervised model selection with applications to speech processing

Linear Latent Force Models using Gaussian Processes

Linear Time Feature Selection for Regularized Least-Squares

LLE with low-dimensional neighborhood representation

Local Optimality of User Choices and Collaborative Competitive Filtering

Local Procrustes for Manifold Embedding: A Measure of Embedding Quality and Embedding Algorithms

Locally Regularized Readouts for Echo-state Networks

Loss-sensitive Training of Probabilistic Conditional Random Fields

Low Dimensional Embedding of fMRI datasets

l_p-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers