Scalable Machine Learning for structured high-dimensional outputs
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.
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.