Maksym Andriushchenko

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 45

Top Topics

Categorical Data

Theoretical Analysis

Top Venues

Publications

Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks. CoRR (2024)
Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning. CoRR (2024)
Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, Hamed Hassani, Eric Wong
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. CoRR (2024)
Maksym Andriushchenko, Nicolas Flammarion
Does Refusal Training in LLMs Generalize to the Past Tense? CoRR (2024)
Linara Adilova, Maksym Andriushchenko, Michael Kamp, Asja Fischer, Martin Jaggi
Layer-wise linear mode connectivity. ICLR (2024)
Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion
Is In-Context Learning Sufficient for Instruction Following in LLMs? CoRR (2024)
Javier Rando, Francesco Croce, Krystof Mitka, Stepan Shabalin, Maksym Andriushchenko, Nicolas Flammarion, Florian Tramèr
Competition Report: Finding Universal Jailbreak Backdoors in Aligned LLMs. CoRR (2024)
Andy Zou, Long Phan, Justin Wang, Derek Duenas, Maxwell Lin, Maksym Andriushchenko, Rowan Wang, Zico Kolter, Matt Fredrikson, Dan Hendrycks
Improving Alignment and Robustness with Circuit Breakers. CoRR (2024)
Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion
A modern look at the relationship between sharpness and generalization. CoRR (2023)
Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion
A Modern Look at the Relationship between Sharpness and Generalization. ICML (2023)
Maksym Andriushchenko, Dara Bahri, Hossein Mobahi, Nicolas Flammarion
Sharpness-Aware Minimization Leads to Low-Rank Features. NeurIPS (2023)
Maksym Andriushchenko, Dara Bahri, Hossein Mobahi, Nicolas Flammarion
Sharpness-Aware Minimization Leads to Low-Rank Features. CoRR (2023)
Klim Kireev, Maksym Andriushchenko, Carmela Troncoso, Nicolas Flammarion
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings. CoRR (2023)
Maksym Andriushchenko, Francesco D'Angelo, Aditya Varre, Nicolas Flammarion
Why Do We Need Weight Decay in Modern Deep Learning? CoRR (2023)
Edoardo Debenedetti, Zishen Wan, Maksym Andriushchenko, Vikash Sehwag, Kshitij Bhardwaj, Bhavya Kailkhura
Scaling Compute Is Not All You Need for Adversarial Robustness. CoRR (2023)
Klim Kireev, Maksym Andriushchenko, Carmela Troncoso, Nicolas Flammarion
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings. NeurIPS (2023)
Sungbin Shin, Dongyeop Lee, Maksym Andriushchenko, Namhoon Lee
The Effects of Overparameterization on Sharpness-aware Minimization: An Empirical and Theoretical Analysis. CoRR (2023)
Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion
SGD with Large Step Sizes Learns Sparse Features. ICML (2023)
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)
Maksym Andriushchenko, Aditya Varre, Loucas Pillaud-Vivien, Nicolas Flammarion
SGD with large step sizes learns sparse features. CoRR (2022)
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 (2022)
Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion
On the effectiveness of adversarial training against common corruptions. UAI (2022)
Maksym Andriushchenko, Nicolas Flammarion
Towards Understanding Sharpness-Aware Minimization. CoRR (2022)
Maksym Andriushchenko, Nicolas Flammarion
Towards Understanding Sharpness-Aware Minimization. ICML (2022)
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)
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)
Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines. ICLR (2021)
Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion
On the effectiveness of adversarial training against common corruptions. CoRR (2021)
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 (2020)
Maksym Andriushchenko, Nicolas Flammarion
Understanding and Improving Fast Adversarial Training. NeurIPS (2020)
Maksym Andriushchenko, Nicolas Flammarion
Understanding and Improving Fast Adversarial Training. CoRR (2020)
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein
RobustBench: a standardized adversarial robustness benchmark. CoRR (2020)
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein
Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search. ECCV (23) (2020)
Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines. CoRR (2020)
Maksym Andriushchenko, Matthias Hein
Provably robust boosted decision stumps and trees against adversarial attacks. NeurIPS (2019)
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)
Francesco Croce, Maksym Andriushchenko, Matthias Hein
Provable Robustness of ReLU networks via Maximization of Linear Regions. AISTATS (2019)
Maksym Andriushchenko, Matthias Hein
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks. CoRR (2019)
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein
Square Attack: a query-efficient black-box adversarial attack via random search. CoRR (2019)
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)
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 (2018)
Marius Mosbach, Maksym Andriushchenko, Thomas Alexander Trost, Matthias Hein, Dietrich Klakow
Logit Pairing Methods Can Fool Gradient-Based Attacks. CoRR (2018)
Francesco Croce, Maksym Andriushchenko, Matthias Hein
Provable Robustness of ReLU networks via Maximization of Linear Regions. CoRR (2018)
Matthias Hein, Maksym Andriushchenko
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. NIPS (2017)
Matthias Hein, Maksym Andriushchenko
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. CoRR (2017)