Simons Institute | An overview of classical robust statistics and generalizations to the future @SimonsInstituteTOC | Uploaded 1 week ago | Updated 1 minute ago
Po-Ling Loh (University of Cambridge)
https://simons.berkeley.edu/talks/po-ling-loh-university-cambridge-2024-08-28
Modern Paradigms in Generalization Boot Camp
Robust statistics aims to provide methods for reliable estimation and inference when data are generated from a distribution with some form of contamination. In this talk, we will provide a self-contained overview of some key concepts in classical robust statistics. We will then discuss a few recent results where classical concepts have proven useful in more modern settings, including heterogenous distributions, new forms of contamination, and private hypothesis testing.
Po-Ling Loh (University of Cambridge)
https://simons.berkeley.edu/talks/po-ling-loh-university-cambridge-2024-08-28
Modern Paradigms in Generalization Boot Camp
Robust statistics aims to provide methods for reliable estimation and inference when data are generated from a distribution with some form of contamination. In this talk, we will provide a self-contained overview of some key concepts in classical robust statistics. We will then discuss a few recent results where classical concepts have proven useful in more modern settings, including heterogenous distributions, new forms of contamination, and private hypothesis testing.
