Statistics at UC Berkeley

Hubeyb Gurdogan, Florida State University (Speaker)
Dec 1, 2020 11:00am Online
ABSTRACT: The GPS (Goldberg, Papanicolaou, Shkolnik) method shrinks the leading eigenvector of the sample covariance matrix towards the vector of all 1’s by a data driven amount in the low sample-high dimension regime. That creates an estimate of betas that has lower l_2 error and significantly reduces the impact of the estimation error on minimum variance portfolio weights and risk forecasts. We...
Benjamin Gunby, Harvard University
Dec 2, 2020 3:10pm Zoom link: Evans Hall
Fix a graph K. What is the probability that a large random graph contains many more copies of K than expected? We discuss this question when our random graph model is that of a sparse random regular graph.
Lester Mackey, Microsoft Research
Dec 2, 2020 4:00pm Evans Hall
This is the story of my assorted attempts to do some good with machine learning (and statistics). Through its telling, I’ll highlight several models of organizing social good efforts, describe half a dozen social good problems that would benefit from our community's attention, and present both resources and challenges for those looking to do some good with ML and stats.

Statistics at UC Berkeley: We are a community engaged in research and education in probability and statistics. In addition to developing fundamental theory and methodology, we are actively involved in statistical problems that arise in such diverse fields as molecular biology, geophysics, astronomy, AIDS research, neurophysiology, sociology, political science, education, demography, and the U.S. Census. We have forged strong interdisciplinary links with other departments and areas of study, particularly biostatistics, mathematics, computer science, and biology, and actively seek to recruit graduate students and faculty who can help to build and maintain such links. We also offer a statistical consulting service each semester.