Robustness in High-dimensional Statistics and Machine Learning
Synopsis
Today’s data pose unprecedented challenges to statisticians and data analysts. It may be incomplete, corrupted, or exposed to some unknown source of contamination. We need new methods and theories to grapple with these challenges. While the rich field of robust statistics addresses some of these questions, there are many new foundational challenges – both statistical and computational, that are posed by high-dimensional data. The goal is to explore several theoretical frameworks and directions towards designing estimators and learning algorithms that are tolerant to errors, contamination, and misspecification in data.
Organizers
- Aravindan Vijayaraghavan (Northwestern University)
- Chao Gao (University of Chicago)
- Yu Cheng (University of Illinois at Chicago)
Graduate Courses
The following graduate course will be offered during this special quarter.
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Friday 2:00-4:50pm, Northwestern Univ., Prof. Aravindan Vijayaraghavan
Participant Registration
Anyone who is interested in participating in the Fall 2021 Special Quarter on Robustness in High-dimensional Statistics and Machine Learning should fill out this form.
Workshops
- September 21: Kickoff event for Fall Special Quarter on “Robustness in high dimensional statistics and machine learning” Yu Cheng, Chao Gao and Aravindan Vijayaraghavan.
- October 19th (Tuesday): Mini-workshop on Statistical and Computational Aspects of Robustness in High-dimensional Estimation
- November 16th (Tuesday): Mini-workshop on New directions on Robustness in ML
Visit the events page to see a full list of upcoming workshops.
Calendar
April 2024 |
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