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ORCIDMark Niklas Müller
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2020 – today
- 2024
[b1]Mark Niklas Müller:
Training and Certification of Neural Networks with Guarantees. ETH Zurich, Zürich, Switzerland, 2024- [c11]Maximilian Baader, Mark Niklas Müller, Yuhao Mao, Martin T. Vechev:
Expressivity of ReLU-Networks under Convex Relaxations. ICLR 2024 - [c10]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Understanding Certified Training with Interval Bound Propagation. ICLR 2024 - [c9]Luca Beurer-Kellner, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Prompt Sketching for Large Language Models. ICML 2024 - [i23]Momchil Peychev, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Automated Classification of Model Errors on ImageNet. CoRR abs/2401.02430 (2024) - [i22]Jasper Dekoninck, Mark Niklas Müller, Maximilian Baader, Marc Fischer, Martin T. Vechev:
Evading Data Contamination Detection for Language Models is (too) Easy. CoRR abs/2402.02823 (2024) - [i21]Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev:
SPEAR: Exact Gradient Inversion of Batches in Federated Learning. CoRR abs/2403.03945 (2024) - [i20]Stefan Balauca, Mark Niklas Müller, Yuhao Mao, Maximilian Baader, Marc Fischer, Martin T. Vechev:
Overcoming the Paradox of Certified Training with Gaussian Smoothing. CoRR abs/2403.07095 (2024) - [i19]Ivo Petrov, Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev:
DAGER: Exact Gradient Inversion for Large Language Models. CoRR abs/2405.15586 (2024) - [i18]Jasper Dekoninck, Mark Niklas Müller, Martin T. Vechev:
ConStat: Performance-Based Contamination Detection in Large Language Models. CoRR abs/2405.16281 (2024) - [i17]Philip Sosnin, Mark Niklas Müller, Maximilian Baader, Calvin Tsay, Matthew Wicker:
Certified Robustness to Data Poisoning in Gradient-Based Training. CoRR abs/2406.05670 (2024) - [i16]Niels Mündler, Mark Niklas Müller, Jingxuan He, Martin T. Vechev:
Code Agents are State of the Art Software Testers. CoRR abs/2406.12952 (2024) - [i15]Anton Alexandrov, Veselin Raychev, Mark Niklas Müller, Ce Zhang, Martin T. Vechev, Kristina Toutanova:
Mitigating Catastrophic Forgetting in Language Transfer via Model Merging. CoRR abs/2407.08699 (2024) - 2023
- [j3]Mark Niklas Müller
, Marc Fischer, Robin Staab, Martin T. Vechev:
Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks. Proc. ACM Program. Lang. 7(PLDI): 786-810 (2023) - [j2]Christopher Brix, Mark Niklas Müller, Stanley Bak, Taylor T. Johnson, Changliu Liu:
First three years of the international verification of neural networks competition (VNN-COMP). Int. J. Softw. Tools Technol. Transf. 25(3): 329-339 (2023) - [c8]Mark Niklas Müller, Franziska Eckert, Marc Fischer, Martin T. Vechev:
Certified Training: Small Boxes are All You Need. ICLR 2023 - [c7]Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Efficient Certified Training and Robustness Verification of Neural ODEs. ICLR 2023 - [c6]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Connecting Certified and Adversarial Training. NeurIPS 2023 - [c5]Momchil Peychev, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Automated Classification of Model Errors on ImageNet. NeurIPS 2023 - [i14]Christopher Brix, Mark Niklas Müller, Stanley Bak, Taylor T. Johnson, Changliu Liu:
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP). CoRR abs/2301.05815 (2023) - [i13]Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Efficient Certified Training and Robustness Verification of Neural ODEs. CoRR abs/2303.05246 (2023) - [i12]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
TAPS: Connecting Certified and Adversarial Training. CoRR abs/2305.04574 (2023) - [i11]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Understanding Certified Training with Interval Bound Propagation. CoRR abs/2306.10426 (2023) - [i10]Maximilian Baader, Mark Niklas Müller, Yuhao Mao, Martin T. Vechev:
Expressivity of ReLU-Networks under Convex Relaxations. CoRR abs/2311.04015 (2023) - [i9]Luca Beurer-Kellner, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Prompt Sketching for Large Language Models. CoRR abs/2311.04954 (2023) - 2022
- [j1]Mark Niklas Müller, Gleb Makarchuk, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
PRIMA: general and precise neural network certification via scalable convex hull approximations. Proc. ACM Program. Lang. 6(POPL): 1-33 (2022) - [c4]Claudio Ferrari, Mark Niklas Müller, Nikola Jovanovic, Martin T. Vechev:
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound. ICLR 2022 - [c3]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Boosting Randomized Smoothing with Variance Reduced Classifiers. ICLR 2022 - [c2]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
(De-)Randomized Smoothing for Decision Stump Ensembles. NeurIPS 2022 - [i8]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Robust and Accurate - Compositional Architectures for Randomized Smoothing. CoRR abs/2204.00487 (2022) - [i7]Claudio Ferrari, Mark Niklas Müller, Nikola Jovanovic, Martin T. Vechev:
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound. CoRR abs/2205.00263 (2022) - [i6]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
(De-)Randomized Smoothing for Decision Stump Ensembles. CoRR abs/2205.13909 (2022) - [i5]Mark Niklas Müller, Franziska Eckert, Marc Fischer, Martin T. Vechev:
Certified Training: Small Boxes are All You Need. CoRR abs/2210.04871 (2022) - [i4]Mark Niklas Müller, Christopher Brix, Stanley Bak, Changliu Liu, Taylor T. Johnson:
The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Results. CoRR abs/2212.10376 (2022) - 2021
- [c1]Mark Niklas Müller, Mislav Balunovic, Martin T. Vechev:
Certify or Predict: Boosting Certified Robustness with Compositional Architectures. ICLR 2021 - [i3]Mark Niklas Müller, Gleb Makarchuk, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Precise Multi-Neuron Abstractions for Neural Network Certification. CoRR abs/2103.03638 (2021) - [i2]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Boosting Randomized Smoothing with Variance Reduced Classifiers. CoRR abs/2106.06946 (2021) - [i1]Mark Niklas Müller, Robin Staab, Marc Fischer, Martin T. Vechev:
Effective Certification of Monotone Deep Equilibrium Models. CoRR abs/2110.08260 (2021)
Coauthor Index
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last updated on 2024-09-04 01:23 CEST by the dblp team
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