Faculty Candidate Seminar|
Provable and Practical Algorithms for Non-convex Problems in Machine Learning
Tuesday, March 27, 2018|
10:30am - 11:30am
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About the Event
Machine learning has become one of the most exciting research areas in the world, with various applications. However, there exists a noticeable gap between theory and practice. On one hand, simple algorithm like stochastic gradient descent (SGD) works very well in practice, without satisfactory theoretical explanations. On the other hand, the algorithms from the theory community, although with solid guarantees, tend to be less efficient compared with the techniques widely used in practice, which are usually hand tuned or ad hoc based on intuition.
Yang Yuan is a sixth year CS PhD candidate at Cornell University, advised by Professor Robert Kleinberg. He did his undergraduate study at Peking University (2008-2012). He was a visiting student at MIT/Microsoft New England (2014-2015), and Princeton University (2016 Fall). He works on the topics at the intersection of machine learning, theory and optimization.
Open to: Public