All Articles

Statistical Learning Theory Working Group

The Statistical Learning Theory working group will meet on Wednesdays. We will have readings, presentations and discussions on topics including (but, not limited to) statistical learning theory, nonparametric estimation and inference, deep learning, functional data analysis, topological data analysis and algebraic statistics. The focus of the group is to read and discuss important papers in one particular topic of interest for a semester or two.

Time Wednesday 2:15 - 3:45 PM
Location Zoom


This semester we will focus on stochastic optimization. The papers we will focus on are categorized below.

  1. Sampling and Gradient Flow:

  2. Langevin Monte Carlo:

  3. Optimization:

Here is an overview of gradient flows by Filippo Santambrogio, introductory lectures on convex optimization by Yurii Nesterov and the more exhaustive lectures on convex optimization by Yurii Nesterov.

The webpage and resources for Fall 2019 can be found here.


The schedule is available on the STAG Google Calendar

If you’re interested in attending the meetings, please sign-up here, and send an email to the L-STAT-STAG with the subject “Add Me” and include your name and department in the body.