STOR Colloquium: Xi Chen, NYU
New York University
Statistical Inference for Model Parameters with Stochastic Gradient Descent
In this talk, we investigate the problem of statistical inference of the true model parameters based on stochastic gradient descent (SGD) with Ruppert-Polyak averaging. To this end, we propose a consistent estimator of the asymptotic covariance of the average iterate from SGD — batch-means estimator, which only uses the iterates from SGD. As the SGD process forms a time-inhomogeneous Markov chain, our batch-means estimator with carefully chosen increasing batch sizes generalizes the classical batch-means estimator designed for time-homogenous Markov chains. The proposed batch-means estimator allows us to construct asymptotically exact confidence intervals and hypothesis tests. We further discuss an extension to conducting inference based on SGD for high-dimensional linear regression.
Bio: Xi Chen is an assistant professor at Stern School of Business at New York University. Before that, he was a Postdoc in the group of Prof. Michael Jordan at UC Berkeley. He obtained his Ph.D. from the Machine Learning Department at Carnegie Mellon University. He studies high-dimensional statistics, multi-armed bandits, and stochastic optimization. He received Simons-Berkeley Research Fellowship, Google Faculty Award, Adobe Data Science Award, Bloomberg research award, and was featured in 2017 Forbes list of “30 Under30 in Science”.
Refreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall