Smooth, Finite, and Convex Optimization Deep Learning Summer School
Beyond Stochastic Gradient Descent
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
Optimization in Learning and Data Analysis
Surrogate Assisted Optimization Methods: Recent Developments and Challenges
Theory
Optimization in Machine Learning: Recent Developments and Current Challenges
Non Smooth, Non Finite, and Non Convex Optimization
Sharp analysis of low-rank kernel matrix approximations
Feature selection, fundamentals and applications