74 citations · 132 across the 14 of their papers we have counts for
9 papers · 1 filter
Average Certified Radius is a Poor Metric for Randomized Smoothing
Chenhao Sun, Yuhao Mao, Mark Niklas Müller +1
Randomized smoothing (RS) is popular for providing certified robustness guarantees against adversarial attacks. The average certified radius (ACR) has emerged as a widely used metr…
Mitigating Catastrophic Forgetting in Language Transfer via Model Merging
Anton Alexandrov, Veselin Raychev, Mark Niklas Müller +3
As open-weight large language models (LLMs) achieve ever more impressive performances across a wide range of tasks in English, practitioners aim to adapt these models to different…
Certification for Differentially Private Prediction in Gradient-Based Training
Matthew Wicker, Philip Sosnin, Igor Shilov +5
We study private prediction where differential privacy is achieved by adding noise to the outputs of a non-private model. Existing methods rely on noise proportional to the global…
Certified Robustness to Data Poisoning in Gradient-Based Training
Philip Sosnin, Mark N. Müller, Maximilian Baader +2
Modern machine learning pipelines leverage large amounts of public data, making it infeasible to guarantee data quality and leaving models open to poisoning and backdoor attacks. P…
DAGER: Exact Gradient Inversion for Large Language Models
Ivo Petrov, Dimitar I. Dimitrov, Maximilian Baader +2
Federated learning works by aggregating locally computed gradients from multiple clients, thus enabling collaborative training without sharing private client data. However, prior w…
Expressivity of ReLU-Networks under Convex Relaxations
Maximilian Baader, Mark Niklas Müller, Yuhao Mao +1
Convex relaxations are a key component of training and certifying provably safe neural networks. However, despite substantial progress, a wide and poorly understood accuracy gap to…
The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Results
Mark Niklas Müller, Christopher Brix, Stanley Bak +2
This report summarizes the 3rd International Verification of Neural Networks Competition (VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled Autono…
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound
Claudio Ferrari, Mark Niklas Muller, Nikola Jovanovic +1
State-of-the-art neural network verifiers are fundamentally based on one of two paradigms: either encoding the whole verification problem via tight multi-neuron convex relaxations…
Boosting Randomized Smoothing with Variance Reduced Classifiers
Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer +1
Randomized Smoothing (RS) is a promising method for obtaining robustness certificates by evaluating a base model under noise. In this work, we: (i) theoretically motivate why ensem…