http://videolectures.net/nips2010_wright_oaml/

Prof. Stephen J. Wright conducted an excellent tutorial in NIPS 2010. This tutorial peeks into several important aspects of algorithms that are useful to practical and large-scale optimization problems in machine learning. Besides high-level overview of each aspect, the talk provides pointers to key references. Topics covered are:

First-order Methods

Stochastic and Incremental Gradient Methods

Shrinking/Thresholding for Regularized Formulations

Optimal Manifolds Identification and High-Order Methods

Decomposition and Coordinate Relaxation

Also some tutorials/talks of interest from the long-term program “Modern Trends in Optimization and Its Application” (Sep – Dec 2010) in UCLA (provided the slides are released).

(Tutorial) Algorithms for Sparse Optimization

(Tutorial) Introduction to Robust Optimization

(Talk) Accelerating First-Order Methods for Large-Scale Well-Structured Convex Optimization

(Talk) Rank-Sparsity Minimization and Latent Variable Graphical Model Selection

(Talk) Recent Advances in Alternating Direction Method: Practice and Theory

(Talk) Bundle-Type Methods Uniformly Optimal for Smooth and Non-Smooth Convex Optimization

(Talk) Weak Recovery Conditions Using Graph Partitioning Bounds

(Talk) A Majorized Penalty Approach for Calibrating Rank Constrained Correlation Matrix Problems