Maksym Andriushchenko | DeepAI

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Featured Co-authors

Matthias Hein
83 publications
Dietrich Klakow
69 publications
Prateek Mittal
63 publications
Nicolas Flammarion
44 publications
Carmela Troncoso
39 publications
Mung Chiang
34 publications
John Collomosse
34 publications
Dara Bahri
22 publications
Francesco Croce
21 publications
Hossein Mobahi
18 publications
Marius Mosbach
17 publications

∙ 06/06/2023

Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings

Research on adversarial robustness is primarily focused on image and tex...

0 Klim Kireev, et al. ∙

∙ 05/25/2023

Sharpness-Aware Minimization Leads to Low-Rank Features

Sharpness-aware minimization (SAM) is a recently proposed method that mi...

1 Maksym Andriushchenko, et al. ∙

∙ 02/14/2023

A modern look at the relationship between sharpness and generalization

Sharpness of minima is a promising quantity that can correlate with gene...

0 Maksym Andriushchenko, et al. ∙

∙ 10/11/2022

SGD with large step sizes learns sparse features

We showcase important features of the dynamics of the Stochastic Gradien...

0 Maksym Andriushchenko, et al. ∙

∙ 06/13/2022

Towards Understanding Sharpness-Aware Minimization

Sharpness-Aware Minimization (SAM) is a recent training method that reli...

0 Maksym Andriushchenko, et al. ∙

∙ 02/25/2022

ARIA: Adversarially Robust Image Attribution for Content Provenance

Image attribution – matching an image back to a trusted source – is an e...

0 Maksym Andriushchenko, et al. ∙

∙ 03/03/2021

On the effectiveness of adversarial training against common corruptions

The literature on robustness towards common corruptions shows no consens...

0 Klim Kireev, et al. ∙

∙ 10/19/2020

RobustBench: a standardized adversarial robustness benchmark

Evaluation of adversarial robustness is often error-prone leading to ove...

4 Francesco Croce, et al. ∙

∙ 07/06/2020

Understanding and Improving Fast Adversarial Training

A recent line of work focused on making adversarial training computation...

0 Maksym Andriushchenko, et al. ∙

∙ 06/23/2020

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

A large body of research has focused on adversarial attacks which requir...

0 Francesco Croce, et al. ∙

∙ 06/08/2020

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

Fine-tuning pre-trained transformer-based language models such as BERT h...

15 Marius Mosbach, et al. ∙

∙ 11/29/2019

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

We propose the Square Attack, a new score-based black-box l_2 and l_∞ ad...

0 Maksym Andriushchenko, et al. ∙

∙ 06/08/2019

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks

The problem of adversarial samples has been studied extensively for neur...

0 Maksym Andriushchenko, et al. ∙

∙ 12/13/2018

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

Classifiers used in the wild, in particular for safety-critical systems,...

52 Matthias Hein, et al. ∙

∙ 10/29/2018

Logit Pairing Methods Can Fool Gradient-Based Attacks

Recently, several logit regularization methods have been proposed in [Ka...

0 Marius Mosbach, et al. ∙

∙ 10/17/2018

Provable Robustness of ReLU networks via Maximization of Linear Regions

It has been shown that neural network classifiers are not robust. This r...

0 Francesco Croce, et al. ∙

∙ 05/23/2017

Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation

Recent work has shown that state-of-the-art classifiers are quite brittl...

0 Matthias Hein, et al. ∙

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