Certify or Predict: Boosting Certified Robustness with Compositional Architectures. - Mark Niklas Müller; Mislav Balunovic; Martin T. Vechev (ICLR, 2021)

Precise Multi-Neuron Abstractions for Neural Network Certification. - Mark Niklas Müller; Gleb Makarchuk; Gagandeep Singh 001; Markus Püschel; Martin T. Vechev (CoRR, 2021)

Effective Certification of Monotone Deep Equilibrium Models. - Mark Niklas Müller; Robin Staab; Marc Fischer 002; Martin T. Vechev (CoRR, 2021)

Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound. - Claudio Ferrari; Mark Niklas Müller; Nikola Jovanovic 001; Martin T. Vechev (ICLR, 2022)

Boosting Randomized Smoothing with Variance Reduced Classifiers. - Miklós Z. Horváth; Mark Niklas Müller; Marc Fischer 002; Martin T. Vechev (ICLR, 2022)

(De-)Randomized Smoothing for Decision Stump Ensembles. - Miklós Z. Horváth; Mark Niklas Müller; Marc Fischer 002; Martin T. Vechev (NeurIPS, 2022)

Robust and Accurate - Compositional Architectures for Randomized Smoothing. - Miklós Z. Horváth; Mark Niklas Müller; Marc Fischer 002; Martin T. Vechev (CoRR, 2022)

The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Results. - Mark Niklas Müller; Christopher Brix; Stanley Bak; Changliu Liu; Taylor T. Johnson (CoRR, 2022)

PRIMA: general and precise neural network certification via scalable convex hull approximations. - Mark Niklas Müller; Gleb Makarchuk; Gagandeep Singh 001; Markus Püschel; Martin T. Vechev (Proc. ACM Program. Lang., 2022)

Certified Training: Small Boxes are All You Need. - Mark Niklas Müller; Franziska Eckert; Marc Fischer 002; Martin T. Vechev (ICLR, 2023)

Efficient Certified Training and Robustness Verification of Neural ODEs. - Mustafa Zeqiri; Mark Niklas Müller; Marc Fischer 002; Martin T. Vechev (ICLR, 2023)

Connecting Certified and Adversarial Training. - Yuhao Mao; Mark Niklas Müller; Marc Fischer 002; Martin T. Vechev (NeurIPS, 2023)

Automated Classification of Model Errors on ImageNet. - Momchil Peychev; Mark Niklas Müller; Marc Fischer 002; Martin T. Vechev (NeurIPS, 2023)

TAPS: Connecting Certified and Adversarial Training. - Yuhao Mao; Mark Niklas Müller; Marc Fischer 002; Martin T. Vechev (CoRR, 2023)

Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks. - Mark Niklas Müller; Marc Fischer 002; Robin Staab; Martin T. Vechev (Proc. ACM Program. Lang., 2023)

Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks - Artifact. - Mark Niklas Müller; Marc Fischer 002; Robin Staab; Martin T. Vechev (Zenodo, 2023)

First three years of the international verification of neural networks competition (VNN-COMP). - Christopher Brix; Mark Niklas Müller; Stanley Bak; Taylor T. Johnson; Changliu Liu (Int. J. Softw. Tools Technol. Transf., 2023)

Expressivity of ReLU-Networks under Convex Relaxations. - Maximilian Baader; Mark Niklas Müller; Yuhao Mao; Martin T. Vechev (ICLR, 2024)

Understanding Certified Training with Interval Bound Propagation. - Yuhao Mao; Mark Niklas Müller; Marc Fischer 002; Martin T. Vechev (ICLR, 2024)

Prompt Sketching for Large Language Models. - Luca Beurer-Kellner; Mark Niklas Müller; Marc Fischer 002; Martin T. Vechev (ICML, 2024)

ConStat: Performance-Based Contamination Detection in Large Language Models. - Jasper Dekoninck; Mark Niklas Müller; Martin T. Vechev (NeurIPS, 2024)

SPEAR: Exact Gradient Inversion of Batches in Federated Learning. - Dimitar I. Dimitrov; Maximilian Baader; Mark Niklas Müller; Martin T. Vechev (NeurIPS, 2024)

SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents. - Niels Mündler; Mark Niklas Müller; Jingxuan He; Martin T. Vechev (NeurIPS, 2024)

DAGER: Exact Gradient Inversion for Large Language Models. - Ivo Petrov; Dimitar I. Dimitrov; Maximilian Baader; Mark Niklas Müller; Martin T. Vechev (NeurIPS, 2024)

Evading Data Contamination Detection for Language Models is (too) Easy. - Jasper Dekoninck; Mark Niklas Müller; Maximilian Baader; Marc Fischer 002; Martin T. Vechev (CoRR, 2024)

Overcoming the Paradox of Certified Training with Gaussian Smoothing. - Stefan Balauca; Mark Niklas Müller; Yuhao Mao; Maximilian Baader; Marc Fischer 002; Martin T. Vechev (CoRR, 2024)

Certified Robustness to Data Poisoning in Gradient-Based Training. - Philip Sosnin; Mark Niklas Müller; Maximilian Baader; Calvin Tsay; Matthew Wicker (CoRR, 2024)

Code Agents are State of the Art Software Testers. - Niels Mündler; Mark Niklas Müller; Jingxuan He; Martin T. Vechev (CoRR, 2024)

Mitigating Catastrophic Forgetting in Language Transfer via Model Merging. - Anton Alexandrov; Veselin Raychev; Mark Niklas Müller; Ce Zhang 001; Martin T. Vechev; Kristina Toutanova (CoRR, 2024)

Average Certified Radius is a Poor Metric for Randomized Smoothing. - Chenhao Sun; Yuhao Mao; Mark Niklas Müller; Martin T. Vechev (CoRR, 2024)

Diagnosing Robotics Systems Issues with Large Language Models. - Jordis Emilia Herrmann; Aswath Mandakath Gopinath; Mikael Norrlof; Mark Niklas Müller (CoRR, 2024)

Training and Certification of Neural Networks with Guarantees. - Mark Niklas Müller (2024)

Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations. - Stefan Balauca; Mark Niklas Müller; Yuhao Mao; Maximilian Baader; Marc Fischer 002; Martin T. Vechev (Trans. Mach. Learn. Res., 2025)

Automated Benchmark Generation for Repository-Level Coding Tasks. - Konstantinos Vergopoulos; Mark Niklas Müller; Martin T. Vechev (CoRR, 2025)