Time+Place: Wednesday 13/01/2016 14:30 Room 337-8 Taub Bld.
Title: Scalable Machine Learning for structured high-dimensional outputs
Speaker: Ofer Meshi - CS-Lecture - Note unusual day http://ttic.uchicago.edu/~meshi/
Affiliation: Toyota Technological Institute at Chicago
Host: Ran El-Yaniv


In recent years, machine learning has emerged as an important and 
influential discipline in computer science and engineering. Modern 
applications of machine learning involve reasoning about complex objects 
like images, videos, and large documents. Treatment of such 
high-dimensional data requires the development of new tools, since 
traditional methods in machine learning no longer apply. In this talk I 
will present two recent works in this direction. The first work 
introduces a family of novel and efficient methods for inference and 
learning in structured output spaces. This framework is based on 
applying principles from convex optimization while exploiting the 
special structure of these problems to obtain efficient algorithms. The 
second work studies the success of a certain type of approximate 
inference methods based on linear programming relaxations. In 
particular, it has been observed that such relaxations are often tight 
in real applications, and I will present a theoretical explanation for 
this interesting phenomenon.

Short Bio:

Ofer Meshi is a Research Assistant Professor at the Toyota Technological 
Institute at Chicago. Prior to that he obtained his Ph.D. and M.Sc. in 
Computer Science from the Hebrew University of Jerusalem. His B.Sc. in 
Computer Science and Philosophy is from Tel Aviv University. 
Ofer's research focuses on machine learning, with an emphasis on 
efficient optimization methods for inference and learning with 
high-dimensional structured outputs. During his doctoral studies Ofer 
was a recipient of Google's European Fellowship in Machine Learning.