Events Calendar

Epstein ISE Department Seminar

Speaker: Yuekai Sun, Ph.D. Candidate, Institute for Computational and Mathematical Engineering, Stanford University

Talk Title: Distributed Estimation and Inference for Sparse Regression

Abstract: We address two outstanding challenges in sparse regression: (i) computationally efficient estimation in distributed settings (ii) valid inference for the selected coefficients. The main computational challenge in a distributed setting is harnessing the computational capabilities of all the machines while keeping communication costs low. We devise an approach that requires only a single round of communication among the machines. We show the approach recovers the convergence rate of the (centralized) lasso as long as each machine has access to an adequate number of samples. Turning to the second challenge, we devise an approach to post-selection inference by conditioning on the selected model. In a nutshell, our approach gives inferences with the same frequency interpretation as those given by data/sample splitting, but it is more broadly applicable and more powerful. The validity of our approach also does not depend on the correctness of the selected model; i.e. it gives valid inferences even when the selected model is incorrect.

Joint work with Jason Lee, Qiang Liu, Dennis Sun, Jonathan Taylor


Biography: Yuekai is a fifth year Ph.D. student at Stanford University, advised by Michael Saunders in the operations research group and Jonathan Taylor in the statistics department. His research interests span high-dimensional statistics, machine learning, and mathematical optimization. He received his B.A. in 2010 from Rice University. Outside of school, he is an avid cyclist with the Stanford cycling team.


Host: Daniel J. Epstein Department of Industrial and Systems Engineering

More Information: SEMINAR-Sun.doc

Location: Ethel Percy Andrus Gerontology Center (GER) - 206

Audiences: Everyone Is Invited

Contact: Georgia Lum