Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization

15 September 2022

Papers citing "Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization"

11 / 11 papers shown

Title
Sample Compression Scheme Reductions Idan Attias Steve Hanneke Arvind Ramaswami MQ 34 1 0 16 Oct 2024
A Model for Combinatorial Dictionary Learning and Inference Avrim Blum Kavya Ravichandran CoGe 24 0 0 26 Jul 2024
On the Computability of Robust PAC Learning Pascale Gourdeau Tosca Lechner Ruth Urner 27 2 0 14 Jun 2024
Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization Idan Attias Gintare Karolina Dziugaite Mahdi Haghifam Roi Livni Daniel M. Roy 30 6 0 14 Feb 2024
Optimal Learners for Realizable Regression: PAC Learning and Online Learning Idan Attias Steve Hanneke Alkis Kalavasis Amin Karbasi Grigoris Velegkas 30 21 0 07 Jul 2023
Adversarial Resilience in Sequential Prediction via Abstention Surbhi Goel Steve Hanneke Shay Moran Abhishek Shetty 41 4 0 22 Jun 2023
Impossibility of Characterizing Distribution Learning -- a simple solution to a long-standing problem Tosca Lechner Shai Ben-David 18 5 0 18 Apr 2023
Reliable learning in challenging environments Maria-Florina Balcan Steve Hanneke Rattana Pukdee Dravyansh Sharma OOD 30 4 0 06 Apr 2023
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness Peiyu Xiong Michael W. Tegegn Jaskeerat Singh Sarin Shubhraneel Pal Julia Rubin SILM AAML 32 8 0 17 Mar 2023
The One-Inclusion Graph Algorithm is not Always Optimal Ishaq Aden-Ali Yeshwanth Cherapanamjeri Abhishek Shetty Nikita Zhivotovskiy 21 7 0 19 Dec 2022
Agnostic Sample Compression Schemes for Regression Idan Attias Steve Hanneke A. Kontorovich Menachem Sadigurschi 24 4 0 03 Oct 2018