[Statlist] Research Webinar in Statistics *FRIDAY 18 MARCH 2022* GSEM, University of Geneva

[gsem-support-instituts]

Dear All,

We are pleased to invite you to our next Research Webinar.

Looking forward to seeing you


Organized by Prof. Sebastian Engelke on behalf of the Research Center for Statistics (https://www.unige.ch/gsem/en/research/institutes/rcs/)


FRIDAY 18 MARCH 2022 at 15:15pm
ONLINE
Zoom research webinar: https://unige.zoom.us/j/92924332087?pwd=U1U1NFk4dTFCRHBMeWYrSDBQcXBiQT09
Meeting ID: 929 2433 2087
Passcode: 399192


Game-Theoretic Statistics and Safe Anytime-Valid Inference
Aaditya K. Ramdas, Carnegie Mellon University, USA
https://www.stat.cmu.edu/~aramdas/

ABSTRACT:
This talk will introduce the concept of an e-value (a nonnegative random variable with expectation at most one under the null), and their sequential extension, e-process, which form the foundations of game-theoretic statistics, along with martingales and supermartingales. E-values are an alternative to p-values, that merges frequentist, Bayesian and game-theoretic ways of thinking, and generalizes likelihood ratios and Bayes factors to nonparametric and composite settings. E-values have desirable properties for multiple testing including being automatically robust to arbitrary dependence between tests (https://arxiv.org/abs/2009.02824). To make the abstract concept of an "e-value" more concrete, I will discuss one setting where such e-values arise naturally, which is universal inference with the split likelihood ratio test (https://www.pnas.org/content/117/29/16880). Extensions to estimation do exist, under the terminology "confidence sequences". In case of further interest in these topics, please check out http://stat.cmu.edu/~aramdas/sequential.html.

BIOGRAPHY:
Aaditya Ramdas (PhD, 2015) is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. He was one of the inaugural inductees of the COPSS Leadership Academy, and a recipient of the Bernoulli New Researcher Award. His work is supported by an NSF CAREER Award, an Adobe Faculty Research Award, an ARL Grant on Safe Reinforcement Learning, the Block Center Grant for Technology and Society, amongst several others.

Aaditya's main theoretical and methodological research interests include selective and simultaneous inference (interactive, structured, post-hoc control of false decision rates, etc), game-theoretic statistics (sequential uncertainty quantification, confidence sequences, always-valid p-values, safe anytime-valid inference, e-processes, supermartingales, etc), and distribution-free black-box predictive inference (conformal prediction, calibration, etc). His areas of applied interest include neuroscience, genetics and auditing and his group's work has received multiple best paper awards.

Visit the website: https://www.unige.ch/gsem/en/research/seminars/rcs/