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Towards Understanding Sharpness-Aware Minimization


Jun 13, 2022
Maksym Andriushchenko, Nicolas Flammarion

* The camera-ready version (accepted at ICML 2022) 

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ARIA: Adversarially Robust Image Attribution for Content Provenance


Feb 25, 2022
Maksym Andriushchenko, Xiaoyang Rebecca Li, Geoffrey Oxholm, Thomas Gittings, Tu Bui, Nicolas Flammarion, John Collomosse


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On the effectiveness of adversarial training against common corruptions


Mar 03, 2021
Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion


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RobustBench: a standardized adversarial robustness benchmark


Oct 19, 2020
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein


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Understanding and Improving Fast Adversarial Training


Jul 06, 2020
Maksym Andriushchenko, Nicolas Flammarion


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Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks


Jun 23, 2020
Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion, Matthias Hein


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On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines


Jun 08, 2020
Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow


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Square Attack: a query-efficient black-box adversarial attack via random search


Nov 29, 2019
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein


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Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks


Jun 08, 2019
Maksym Andriushchenko, Matthias Hein


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Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem


Dec 13, 2018
Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf


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