Topic Description

The field of robust statistics addresses the challenge of devising estimators that demonstrate reliable performance in situations where the data deviates substantially from the assumed idealized models. Over the past decade, a line of work in computer science has led to significant advances in the algorithmic aspects of this field. This workshop will concentrate on exploring the upcoming research directions in algorithmic robust statistics, emphasizing connections with different privacy and associated concepts of algorithmic stability.

List of Participants

Hilal Asi (Apple)
Pranjal Awasthi (Google)
Ainesh Bakshi (MIT)
Sivaraman Balakrishnan (CMU)
Gavin Brown (U. Washington)
Sitan Chen (Harvard)
Yu Cheng (Brown)
Ilias Diakonikolas (UW Madison)
Chao Gao (U. Chicago)
Rong Ge (Duke)
Sam Hopkins (MIT)
Ioannis Iakovidis (UW Madison)
He Jia (Northwestern)
Gautam Kamath (Waterloo)
Daniel Kane (UCSD)
Sushrut Karmalkar (UW Madison)
Jasper Lee (UW Madison)
Jerry Li (MSR)
Allen Liu (MIT)
Sihan Liu (UCSD)
Mingchen Ma (UW Madison)
Mahbod Majid (CMU)
Stas Minsker (USC)
Argyris Mouzakis (Waterloo)
Shuo Pang (University of Copenhagen)
Ankit Pensia (IBM)
Thanasis Pittas (UW Madison)
Aaron Potechin (U. Chicago)
Lisheng Ren (UW Madison)
Mahdi Soltanokotabi (USC)
Adam Smith (BU)
Yuxin Sun (UW Madison)
Kevin Tian (UT Austin)
Stefan Tiegel (ETH)
Santosh Vempala (GaTech)
Aravindan Vijayraghavan (Northwestern)
Lydia Zakynthinou (Berkeley)
Nikos Zarifis (UW Madison)

Schedule

The tentative schedule can be found here.