Mittagsseminar (in cooperation with J. Lengler, A. Steger, and D. Steurer)

Date and Time: Thursday, April 16, 2009, 12:15 pm

Duration: This information is not available in the database

Location: OAT S15/S16/S17

Speaker: Shai Ben-David (Univ. of Waterloo, Canada)

New Theoretical Challenges emerging from Machine Learning applications

Statistical machine learning is a fast growing area, focused on automated detection of meaningful patterns in large and complex data sets. Theoretical analysis has played a major role in some of the most prominent practical successes in this field. However, our mainstream machine learning theory assumes some strong simplifying assumptions which are often unrealistic. In the past decade, the practice of machine learning has led to the development of various heuristic paradigms that answer the needs of a vastly growing range of applications. Many useful such paradigms fall beyond the scope of the currently available analysis, raising the need for major extensions of the common theoretical models.

In this talk, I will survey some of these application-motivated theoretical challenges. In particular, I will discuss recent developments in the theoretical analysis of semi-supervised learning, multi-task learning, "learning to learn", privacy-preserving learning and more.

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