Maksym Andriushchenko

CoRR, 2024

2023

Scaling Compute Is Not All You Need for Adversarial Robustness.

[BibT_eX]

[DOI]

Edoardo Debenedetti

Zishen Wan

Vikash Sehwag

Kshitij Bhardwaj

Bhavya Kailkhura

CoRR, 2023

The Effects of Overparameterization on Sharpness-aware Minimization: An Empirical and Theoretical Analysis.

[BibT_eX]

[DOI]

Sungbin Shin

Dongyeop Lee

Namhoon Lee

CoRR, 2023

Why Do We Need Weight Decay in Modern Deep Learning?

[BibT_eX]

[DOI]

Francesco D'Angelo

Aditya Varre

CoRR, 2023

Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings.

[BibT_eX]

[DOI]

Klim Kireev

Carmela Troncoso

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sharpness-Aware Minimization Leads to Low-Rank Features.

[BibT_eX]

[DOI]

Dara Bahri

Hossein Mobahi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

SGD with Large Step Sizes Learns Sparse Features.

[BibT_eX]

[DOI]

Aditya Vardhan Varre

Loucas Pillaud-Vivien

Proceedings of the International Conference on Machine Learning, 2023

A Modern Look at the Relationship between Sharpness and Generalization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

On the effectiveness of adversarial training against common corruptions.

[BibT_eX]

[DOI]

Klim Kireev

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Towards Understanding Sharpness-Aware Minimization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

ARIA: Adversarially Robust Image Attribution for Content Provenance.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks.

[BibT_eX]

[DOI]

Naman D. Singh

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

RobustBench: a standardized adversarial robustness benchmark.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

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

[BibT_eX]

[DOI]

Marius Mosbach

Dietrich Klakow

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

RobustBench: a standardized adversarial robustness benchmark.

[BibT_eX]

[DOI]

CoRR, 2020

Understanding and Improving Fast Adversarial Training.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

Provably robust boosted decision stumps and trees against adversarial attacks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

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

[BibT_eX]

[DOI]

Julian Bitterwolf

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

Provable Robustness of ReLU networks via Maximization of Linear Regions.

[BibT_eX]

[DOI]

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Logit Pairing Methods Can Fool Gradient-Based Attacks.

[BibT_eX]

[DOI]

Marius Mosbach

Thomas Alexander Trost

Dietrich Klakow

CoRR, 2018

2017

Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation.

[BibT_eX]

[DOI]