Events Calendar

CS Colloquium: Rong Ge (Microsoft Research) - Towards Provable and Practical Machine Learning

Speaker: Rong Ge, Microsoft Research

Talk Title: Towards Provable and Practical Machine Learning

Series: CS Colloquium

Abstract: Many problems --- especially machine learning problems like sparse coding or topic modeling --- are hard in the worst-case, but nevertheless solved in practice by algorithms whose convergence properties are not understood. In this talk I will show how we can identify natural properties of "real-life" instances that allow us to design scalable algorithms for a host of well-known machine learning problems. Most of the talk will be focused on the sparse coding problem: a basic task in many fields including signal processing, neuroscience and machine learning where the goal is to learn a basis that enables a sparse representation of a given set of data, if one exists. Here we give a general framework for understanding alternating minimization which we leverage to analyze existing heuristics and to design new ones also with provable guarantees.

The lecture will be available to stream Here

Biography: Rong Ge obtained his Ph.D. at Princeton University, advised by Sanjeev Arora. Currently he is a post-doctoral researcher at Microsoft Research, New England. He is broadly interested in theoretical computer science and machine learning, especially applying algorithm design and analysis techniques to machine learning problems. The key thread running through his work is to identify natural properties of "real-life" instances that allow him to design scalable algorithms for several interesting machine learning problems including topic modeling and sparse coding.

Host: Computer Science Department

More Info: https://bluejeans.com/651721928

Location: Olin Hall of Engineering (OHE) - 132

Audiences: Everyone Is Invited

Contact: Assistant to CS chair

Event Link: https://bluejeans.com/651721928