Maksym Andriushchenko
Maksym Andriushchenko
PhD student, EPFL
Verified email at
Cited by
Cited by
Formal guarantees on the robustness of a classifier against adversarial manipulation
M Hein, M Andriushchenko
NeurIPS 2017, 2017
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
M Hein, M Andriushchenko, J Bitterwolf
CVPR 2019 (oral), 2018
Provable Robustness of ReLU Networks via Maximization of Linear Regions
F Croce*, M Andriushchenko*, M Hein
AISTATS 2019, 2018
Square attack: a query-efficient black-box adversarial attack via random search
M Andriushchenko, F Croce, N Flammarion, M Hein
ECCV 2020, 2019
Logit Pairing Methods Can Fool Gradient-Based Attacks
M Mosbach*, M Andriushchenko*, T Trost, M Hein, D Klakow
NeurIPS 2018 Workshop on Security in Machine Learning, 2018
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
M Andriushchenko, M Hein
NeurIPS 2019, 2019
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
M Mosbach, M Andriushchenko, D Klakow
ICLR 2021, 2020
Understanding and Improving Fast Adversarial Training
M Andriushchenko, N Flammarion
NeurIPS 2020, 2020
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
F Croce, M Andriushchenko, ND Singh, N Flammarion, M Hein
ECCV 2020 Workshop on Adversarial Robustness in the Real World, 2020
RobustBench: a standardized adversarial robustness benchmark
F Croce, M Andriushchenko, V Sehwag, N Flammarion, M Chiang, ...
arXiv preprint arXiv:2010.09670, 2020
Provable Adversarial Defenses for Boosting
M Andriushchenko
Master's thesis, Saarland University, 2019
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