Date: 2015-01-30

Time: 15:30-16:30

Location: BURN 1205

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.

This talk is based on joint work with Jason Lee, Qiang Liu, Dennis Sun, and Jonathan Taylor.

Speaker

Yuekai Sun is a PhD candidate at the Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA.