Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation-Reference-Cited by-同舟云学术

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Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation

Published:2023-12 Issue:12 Volume:69 Page:7909-7952
ISSN:0018-9448
Container-title:IEEE Transactions on Information Theory
language:
Short-container-title:IEEE Trans. Inform. Theory

Author:

Wang Ke¹^ORCID,Muthukumar Vidya²,Thrampoulidis Christos³

Affiliation:

1. Department of Statistics and Applied Probability, University of California at Santa Barbara, Santa Barbara, CA, USA

2. Simons Institute for the Theory of Computing, Berkeley, CA, USA

3. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada

Funder

NSF

Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant

King Abdullah University of Science and Technology

NSF through CAREER Award

Adobe Data Science Research Award

Amazon Research Award

Google Research Collabs Award

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Link

https://ieeexplore.ieee.org/ielam/18/10328924/10266749-aam.pdf

Reference95 articles.

1. The implicit bias of gradient descent on separable data;soudry;J Mach Learn Res,2018

2. A precise high-dimensional asymptotic theory for boosting and min-?1-norm interpolated classifiers;liang;arXiv 2002 01586,2020

3. Rademacher and Gaussian complexities: Risk bounds and structural results;bartlett;J Mach Learn Res,2003

4. On the Precise Error Analysis of Support Vector Machines

5. On the proliferation of support vectors in high dimensions;hsu;Proc 24th Int Conf Artif Intell Statist,2021

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