Maksym Andriushchenko
24 papers · 2017–2025 · 8 venues · across top CS/AI venues
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Research topics
Machine Learning
(19)
Artificial Intelligence
(15)
Core AI
(14)
Optimization & Theory Β· Machine Learning
(12)
Deep Learning
(11)
Learning Types Β· Machine Learning
(10)
Optimization & Theory Β· Deep Learning
(5)
Learning Types Β· Deep Learning
(4)
Natural Language Processing
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Resources & Methods
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Techniques
(2)
Computer Science
(1)
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Keywords
adversarial robustness
(6)
adversarial attack
(5)
neural network
(4)
large language model
(3)
jailbreak attack
(3)
adversarial training
(3)
sharpness-aware minimization
(3)
boosted decision tree
(2)
stochastic gradient descent
(2)
adversarial prompt
(2)
implicit bia
(2)
implicit regularization
(2)
instruction fine-tuning
(2)
model alignment
(2)
adversarial learning
(2)
neural network optimization
(2)
ai safety
(2)
neural network robustness
(2)
in-context learning
(1)
data augmentation
(1)
Papers
AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents
ICLR 2025
Critical Influence of Overparameterization on Sharpness-aware Minimization
UAI 2025
Does Refusal Training in LLMs Generalize to the Past Tense?
ICLR 2025
Is In-Context Learning Sufficient for Instruction Following in LLMs?
ICLR 2025
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks
ICLR 2025
Improving Alignment and Robustness with Circuit Breakers
NeurIPS 2024
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
NeurIPS 2024
Layer-wise linear mode connectivity
ICLR 2024
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning
ICML 2024
Why Do We Need Weight Decay in Modern Deep Learning?
NeurIPS 2024
A Modern Look at the Relationship between Sharpness and Generalization
ICML 2023
SGD with Large Step Sizes Learns Sparse Features
ICML 2023
Sharpness-Aware Minimization Leads to Low-Rank Features
NeurIPS 2023
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings
NeurIPS 2023
On the effectiveness of adversarial training against common corruptions
UAI 2022
Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks
AAAI 2022
Towards Understanding Sharpness-Aware Minimization
ICML 2022
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
ICLR 2021
Square Attack: a query-efficient black-box adversarial attack via random search
ECCV 2020
Understanding and Improving Fast Adversarial Training
NeurIPS 2020
Provable Robustness of ReLU networks via Maximization of Linear Regions
AISTATS 2019
Provably robust boosted decision stumps and trees against adversarial attacks
NeurIPS 2019
Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem
CVPR 2019
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
NeurIPS 2017