Sublinear Optimization for Machine Learning

In many modern optimization problems, particularly those arising in machine learning, the amount data is too large to apply standard convex optimization methods. We'll discuss new optimization algorithms that make use of randomization to prune the data produce a correct solution albeit running in time which is smaller than the data representation, i.e. sublinear running time. We'll present such sublinear-time algorithms for linear classification, support vector machine training, semi-definite programming and other optimization problems.  These new algorithms are based on a primal-dual approach, and use a combination of novel sampling techniques and the randomized implementation of online learning algorithms. We'll describe information-theoretic lower bounds that show our running times to be nearly best possible in the unit-cost RAM model.

The talk will be self contained - no prior knowledge in convex optimization or machine learning is assumed.

Elad Hazan is an associate professor of operations research at the Technion, Israel Institute of Technology. His main research area is machine learning and its relationship to optimization, game theory and computational complexity. Elad received his Ph.D. in computer science from Princeton University. He is the recipient of several best paper awards including the Goldberg Best Paper award (twice), and the European Research Council starting grant.