Yuanzhi Li will present his Pre FPO on Friday, January 19, 2018 at 1:30pm in CS 302. The members of his committee are as follows: Sanjeev Arora (advisor), Elad Hazan (reader), Yingyu Liang (U. Wisconsin, reader), Matt Weinberg (non-reader), Yoram Singer (non-reader) Please see below for talk abstract. All are welcome to join. Abstract: The recent success of machine learning has posed a great challenge for theoreticians: we lack a strong theoretical understanding of non-linear models and non-convex objective functions, which, in turn, are widely used in practice. My research goal is to bridge the gap between theory and practice in machine learning by building new and principled methods to better explain and enhance the empirical success of these models and algorithms. In the first part I will talk about modeling. In particular, I will focus on over-parametrization: A widely used method to build models with more parameters than the number of training examples. I will show, through the example of matrix sensing, how (stochastic) gradient descent can learn an over-parametrized model much faster than the under-parametrized one, without over-fitting the data. In the next part I will talking about algorithms for non-convex machine learning tasks. I will show, through the example of non-negative matrix factorization, how to use special structures of the data to design simple, provable, efficient and practical algorithms for the learning tasks.