Optimization in Machine Learning: Recent Developments and Current Challenges
Lecture 14: Methods (Truncated Newton Method)
Optimization Algorithms in Support Vector Machines
Large Scale Learning - Challenge
Stochastic Subgradient Approach for Solving Linear Support Vector Machines
The First-Order View of Boosting Methods: Computational Complexity and Connections to Regularization
Is Feature Selection Secure against Training Data Poisoning?
Limited-memory quasi-Newton and Hessianfree Newton methods for non-smooth optimization
Tools and Techniques for Sparse Optimization and Beyond
Smooth, Finite, and Convex Optimization Deep Learning Summer School