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ORCIDMaksym Andriushchenko
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2020 – today
- 2023
[i15]Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion:
A modern look at the relationship between sharpness and generalization. CoRR abs/2302.07011 (2023)- 2022
- [c13]Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion, Matthias Hein:
Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks. AAAI 2022: 6437-6445 - [c12]Maksym Andriushchenko, Xiaoyang Rebecca Li, Geoffrey Oxholm, Thomas Gittings, Tu Bui, Nicolas Flammarion, John P. Collomosse:
ARIA: Adversarially Robust Image Attribution for Content Provenance. CVPR Workshops 2022: 33-43 - [c11]Maksym Andriushchenko, Nicolas Flammarion:
Towards Understanding Sharpness-Aware Minimization. ICML 2022: 639-668 - [c10]Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion:
On the effectiveness of adversarial training against common corruptions. UAI 2022: 1012-1021 - [i14]Maksym Andriushchenko, Xiaoyang Rebecca Li, Geoffrey Oxholm, Thomas Gittings, Tu Bui, Nicolas Flammarion, John P. Collomosse:
ARIA: Adversarially Robust Image Attribution for Content Provenance. CoRR abs/2202.12860 (2022) - [i13]Maksym Andriushchenko, Nicolas Flammarion:
Towards Understanding Sharpness-Aware Minimization. CoRR abs/2206.06232 (2022) - [i12]Maksym Andriushchenko, Aditya Varre, Loucas Pillaud-Vivien, Nicolas Flammarion:
SGD with large step sizes learns sparse features. CoRR abs/2210.05337 (2022) - 2021
- [c9]Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow:
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines. ICLR 2021 - [c8]Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein:
RobustBench: a standardized adversarial robustness benchmark. NeurIPS Datasets and Benchmarks 2021 - [i11]Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion:
On the effectiveness of adversarial training against common corruptions. CoRR abs/2103.02325 (2021) - 2020
- [c7]Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein:
Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search. ECCV (23) 2020: 484-501 - [c6]Maksym Andriushchenko, Nicolas Flammarion:
Understanding and Improving Fast Adversarial Training. NeurIPS 2020 - [i10]Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow:
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines. CoRR abs/2006.04884 (2020) - [i9]Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion, Matthias Hein:
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks. CoRR abs/2006.12834 (2020) - [i8]Maksym Andriushchenko, Nicolas Flammarion:
Understanding and Improving Fast Adversarial Training. CoRR abs/2007.02617 (2020) - [i7]Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein:
RobustBench: a standardized adversarial robustness benchmark. CoRR abs/2010.09670 (2020)
2010 – 2019
- 2019
- [c5]Francesco Croce, Maksym Andriushchenko, Matthias Hein:
Provable Robustness of ReLU networks via Maximization of Linear Regions. AISTATS 2019: 2057-2066 - [c4]Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf:
Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem. CVPR 2019: 41-50 - [c3]Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf:
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. CVPR Workshops 2019: 58-74 - [c2]Maksym Andriushchenko, Matthias Hein:
Provably robust boosted decision stumps and trees against adversarial attacks. NeurIPS 2019: 12997-13008 - [i6]Maksym Andriushchenko, Matthias Hein:
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks. CoRR abs/1906.03526 (2019) - [i5]Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein:
Square Attack: a query-efficient black-box adversarial attack via random search. CoRR abs/1912.00049 (2019) - 2018
- [i4]Francesco Croce, Maksym Andriushchenko, Matthias Hein:
Provable Robustness of ReLU networks via Maximization of Linear Regions. CoRR abs/1810.07481 (2018) - [i3]Marius Mosbach, Maksym Andriushchenko, Thomas Alexander Trost, Matthias Hein, Dietrich Klakow:
Logit Pairing Methods Can Fool Gradient-Based Attacks. CoRR abs/1810.12042 (2018) - [i2]Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf:
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. CoRR abs/1812.05720 (2018) - 2017
- [c1]Matthias Hein, Maksym Andriushchenko:
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. NIPS 2017: 2266-2276 - [i1]Matthias Hein, Maksym Andriushchenko:
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. CoRR abs/1705.08475 (2017)
Coauthor Index
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last updated on 2023-02-21 22:53 CET by the dblp team
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