TU1.R1.1

Equivalence of Empirical Risk Minimization to Regularization on the Family of f-Divergences

Francisco Daunas, Iñaki Esnaola, University of Sheffield, United Kingdom; Samir M. Perlaza, INRIA, France; H. Vincent Poor, Princeton University, United States
Session:
Statistical Learning
Track:
8: Machine Learning
Location:
Ballroom II & III
Presentation Time:
Tue, 9 Jul, 09:45 - 10:05
Session Chair:
Meir Feder, Tel-Aviv University
Presentation
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Discussion
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Session TU1.R1
TU1.R1.1: Equivalence of Empirical Risk Minimization to Regularization on the Family of f-Divergences
Francisco Daunas, Iñaki Esnaola, University of Sheffield, United Kingdom; Samir M. Perlaza, INRIA, France; H. Vincent Poor, Princeton University, United States
TU1.R1.2: Error Exponent in Agnostic PAC Learning
Adi Hendel, Meir Feder, Tel Aviv University, Israel
TU1.R1.3: Universal Batch Learning Under The Misspecification Setting
Shlomi Vituri, Meir Feder, Tel Aviv University, Israel
TU1.R1.4: Minimal Communication-Cost Statistical Learning
Milad Sefidgaran, Paris Research Center, Huawei Technologies France, France; Abdellatif Zaidi, Université Gustave Eiffel, France, France; Piotr Krasnowski, Paris Research Center, Huawei Technologies France, France
Resources
No resources available.