| Square attack: a query-efficient black-box adversarial attack via random search M Andriushchenko*, F Croce*, N Flammarion, M Hein ECCV 2020, 2020 | 1018 | 2020 |
| RobustBench: a standardized adversarial robustness benchmark F Croce*, M Andriushchenko*, V Sehwag*, E Debenedetti*, N Flammarion, ... NeurIPS 2021 Datasets and Benchmarks Track, Best Paper Honorable Mention …, 2021 | 683 | 2021 |
| 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), 2019 | 629 | 2019 |
| Formal guarantees on the robustness of a classifier against adversarial manipulation M Hein, M Andriushchenko NeurIPS 2017, 2017 | 609 | 2017 |
| On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines M Mosbach, M Andriushchenko, D Klakow ICLR 2021, 2021 | 398 | 2021 |
| Understanding and Improving Fast Adversarial Training M Andriushchenko, N Flammarion NeurIPS 2020, 2020 | 323 | 2020 |
| Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks M Andriushchenko, M Hein NeurIPS 2019, 2019 | 288 | 2019 |
| Provable Robustness of ReLU Networks via Maximization of Linear Regions F Croce*, M Andriushchenko*, M Hein AISTATS 2019, 2019 | 185 | 2019 |
| Towards Understanding Sharpness-Aware Minimization M Andriushchenko, N Flammarion ICML 2022, 2022 | 128 | 2022 |
| Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks F Croce, M Andriushchenko, ND Singh, N Flammarion, M Hein AAAI 2022, 2022 | 101 | 2022 |
| On the effectiveness of adversarial training against common corruptions K Kireev*, M Andriushchenko*, N Flammarion UAI 2022, 2021 | 101 | 2021 |
| 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 | 78 | 2018 |
| SGD with Large Step Sizes Learns Sparse Features M Andriushchenko, A Varre, L Pillaud-Vivien, N Flammarion ICML 2023, 2022 | 51 | 2022 |
| A Modern Look at the Relationship between Sharpness and Generalization M Andriushchenko, F Croce, M Müller, M Hein, N Flammarion ICML 2023, 2023 | 43 | 2023 |
| Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks M Andriushchenko, F Croce, N Flammarion ICML 2024 Workshop on the Next Generation of AI Safety, 2024 | 35 | 2024 |
| JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models P Chao*, E Debenedetti*, A Robey*, M Andriushchenko*, F Croce, ... NeurIPS 2024 Datasets and Benchmarks Track, 2024 | 30 | 2024 |
| Sharpness-Aware Minimization Leads to Low-Rank Features M Andriushchenko, D Bahri, H Mobahi, N Flammarion NeurIPS 2023, 2023 | 21 | 2023 |
| Why Do We Need Weight Decay in Modern Deep Learning? M Andriushchenko*, F D'Angelo*, A Varre, N Flammarion NeurIPS 2024, 2024 | 15 | 2024 |
| Layerwise Linear Mode Connectivity L Adilova, M Andriushchenko, M Kamp, A Fischer, M Jaggi ICLR 2024, 2024 | 12 | 2024 |
| Long is more for alignment: A simple but tough-to-beat baseline for instruction fine-tuning H Zhao, M Andriushchenko, F Croce, N Flammarion ICML 2024, 2024 | 10 | 2024 |
Nicolas FlammarionEPFLVerified email at epfl.ch
