Neyman Seminar

The Neyman seminar is the statistics seminar in the Department. Historically, it has been focused on applications of Statistics to other fields. Nowadays, it has a very broad scope, with topics ranging from applications of statistics to theory.

The seminar is held on Wednesdays from 4 to 5 in the Jerzy Neyman room, 1011 Evans.

Details of individual seminar events are published in the campus' event system.

You can sign up to the department's seminars@stat mailing list to receive related announcements.

Add this series of events to your calendar: ICAL or XML

Recent & Upcoming Neyman Seminars

Rina Foygel Barber, University of Chicago (Speaker)
Sep 2, 2020 4:00pm
Seminar on Zoom Evans Hall
Abstract:
Speaker: Rina Foygel Barber, University of Chicago. Title: Is distribution-free inference possible for binary regression? See "https://statistics.berkeley.edu/research/seminars/neyman" for signing up the mailing list and for the Zoom link of the event.
Mark Newman, University of Michigan
Sep 9, 2020 4:00pm
Zoom seminar Evans Hall
Abstract:
Abstract: Most networks and graphs encountered in empirical studies, including the Internet and the World Wide Web, social networks, and biological and ecological networks, are very sparse. Standard spectral and linear algebra methods perform poorly when applied to such networks. Message passing methods, such as belief propagation, offer an alternative that works well in the sparse limit and can...
Nancy Zhang, University of Pennsylvania
Sep 16, 2020 4:00pm
Zoom seminar Evans Hall
Abstract:
Abstract: Cells are the basic biological units of multicellular organisms. The development of single-cell RNA sequencing (scRNA-seq) technologies have enabled us to study the diversity of cell types in tissue and to elucidate the roles of individual cell types in disease. Yet, scRNA-seq data are noisy and sparse, with only a small proportion of the transcripts that are present in each cell...
Sam Pimentel, UC Berkeley, Weijie Su, University of Pennsylvania
Sep 23, 2020 1:30pm
Evans Hall
Abstract:
Title: Local Elasticity: A Phenomenological Approach Toward Understanding Deep Learning Speaker: Weijie Su Title: The uniform general signed rank test and its design sensitivity Speaker: Sam Pimentel
Mark Risser, Lawrence Berkeley National Laboratory
Sep 30, 2020 4:00pm
Zoom id: 97648161149. No passcode. Evans Hall
Abstract:
Abstract: In spite of the diverse literature on nonstationary spatial modelling and approximate Gaussian process (GP) methods, there are no general approaches for conducting fully Bayesian inference for moderately sized nonstationary spatial data sets on a personal laptop. For statisticians and data scientists who wish to conduct posterior inference and prediction with appropriate uncertainty...
Misha Belkin, UC San Diego
Oct 7, 2020 4:00pm
Zoom id: 97648161149. No passcode. Evans Hall
Abstract:
Analyses based on the Empirical Risk Minimization (ERM) and uniform laws of large numbers have long served as the main theoretical foundation for supervised machine learning. Yet, even early on, they had to contend with phenomena such as lack of over-fitting in boosting. Perhaps even more troubling was the fact that one nearest neighbor, the simplest and most intuitive classifier, did not fit...
Jose R. Zubizarreta, Harvard University
Oct 14, 2020 4:00pm
Zoom id: 97648161149. No passcode. Evans Hall
Abstract:
Abstract: In observational studies of causal effects, matching methods are widely used to approximate the ideal study that would be conducted under controlled experimentation. In this talk, I will discuss new matching methods that use tools from modern optimization to overcome five limitations of standard matching approaches. In particular, these new matching methods (i) directly obtain flexible...
Aaron Roth, University of Pennsylvania
Oct 21, 2020 4:00pm
Evans Hall
Abstract:
Abstract: We show how to achieve multi-calibrated estimators not just for means, but also for variances and other higher moments. Informally, this means that we can find regression functions which, given a data point, can make point predictions not just for the expectation of its label, but for higher moments of its label distribution as well --- and those predictions match the true distribution...
Peng Ding, UC Berkeley
Oct 22, 2020 4:00pm
Evans Hall
Abstract:
Fisher’s randomization test delivers exact p-values under the strong null hypothesis of no treatment effect on any units whatsoever and allows for flexible covariate adjustment to improve the power. Of interest is whether the procedure could also be valid for testing the weak null hypothesis of zero average treatment effect. Towards this end, we evaluate two general strategies for Fisher...
Susan Murphy, Harvard University
Oct 28, 2020 4:00pm
Evans Hall
Yihong Wu, Yale University
Nov 4, 2020 4:00pm
Evans Hall
Will Fithian, University of California, Berkeley
Nov 10, 2020 4:30pm
Evans Hall