Introduction to convex programming, interior point methods, and semi-definite programming
Optimization in Machine Learning: Recent Developments and Current Challenges
Optimization Algorithms in Support Vector Machines
Prediction: Machine Learning and Statistics
Convex Analysis and Optimization
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
Primal-Dual Subgradient Methods for Huge-Scale Problems
Limited-memory quasi-Newton and Hessianfree Newton methods for non-smooth optimization
Optimal Computational Trade-Off of Inexact Proximal Methods
Active Set Algorithm for Structured Sparsity-Inducing Norm