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BEGIN:VEVENT
DTSTAMP:20260404T174135Z
DTSTART;VALUE=DATE-TIME:20230516T160000
DTEND;VALUE=DATE-TIME:20230516T170000
SUMMARY:YRM Week 4 - Mengchu Li on Robustness\, local differential privac
 y and change point analysis
TZID:Europe/London
UID:20230516-8a1785d7880083fb01881ecafa171cbd@warwick.ac.uk
CREATED:20230523T150700Z
DESCRIPTION:Title: Robustness\, local differential privacy and change poi
 nt analysis Abstract: Machine learning and statistical algorithms are no
 w implemented at a large scale in almost every aspect of our society\, s
 ignificantly impacting our daily lives through their performance. Hence\
 , there is a soaring demand for the development of trustworthy procedure
 s. The three topics in the title are broadly related to the issue of tru
 st. At a high level\, robustness refers to the performance of a procedur
 e in the presence of outliers and/or heavy-tailed errors\, while preserv
 ing privacy constrains how much information can be shared from potential
 ly sensitive raw data. Change points\, on the other hand\, are useful in
  modelling non-stationary time series data with potential application in
  detecting abnormal behaviours in data streams. With an appropriate leve
 l of introduction to each topic in the title\, I will present my thesis 
 which contains three works at their intersection. The titles of these th
 ree papers are: Adversarially robust change point detection\, Network ch
 ange point localisation under local differential privacy\, and On robust
 ness and local differential privacy. They are accepted by NeurIPS 2021\,
  NeurIPS 2022 and the Annals of Statistics\, respectively.
LOCATION:
CATEGORIES:
LAST-MODIFIED:20230523T150700Z
ORGANIZER;CN=Claudia Viaro:
END:VEVENT
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