Constrained Learning With Non-Convex Losses

Author:

Chamon Luiz F. O.1ORCID,Paternain Santiago2ORCID,Calvo-Fullana Miguel3ORCID,Ribeiro Alejandro4ORCID

Affiliation:

1. Simons Institute for the Theory of Computing, University of California at Berkeley, Berkeley, CA, USA

2. Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA

3. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA

4. Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA

Funder

NSF-Simons MoDL

NSF HDR TRIPODS

ARL DCIST

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Reference85 articles.

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3. Preventing fairness gerrymandering: Auditing and learning for subgroup fairness;kearns;Proc 35th Int Conf Mach Learn,2018

4. Rademacher and Gaussian complexities: Risk bounds and structural results;bartlett;J Mach Learn Res,2002

5. Towards deep learning models resistant to adversarial attacks;ma?dry;Proc Int Conf Learn Represent,2018

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