An Equivalence between the Lasso and Support Vector Machines. Regularized Dictionary Learning. Hybrid Conditional Gradient-Smoothing Algorithms with Applications to Sparse and Low Rank Regularization. Nonconvex Proximal Splitting with Computational Errors. Learning Constrained Task Similarities in Graph-Regularized Multi-Task Learning. The Graph-Guided Group Lasso for Genome-Wide Association Studies. On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex Functions. Detecting Ineffective Features for Nonparametric Regression. Quadratic Basis Pursuit. Robust Compressive Sensing. Regularized Robust Portfolio Estimation. The Why and How of Nonnegative Matrix Factorization. Rank Constrained Optimization Problems in Computer Vision. Low-Rank Tensor Denoising and Recovery via Convex Optimization. Learning Sets and Subspaces. Output Kernel Learning Methods. Kernel Based Identification of Systems with Multiple Outputs Using Nuclear Norm Regularization. Kernel Methods for Image Denoising. Single-Source Domain Adaptation with Target and Conditional Shift. Multi-Layer Support Vector Machines. Online Regression with Kernels.