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Maximilian Baader
Maximilian Baader
ETH Zurich
Verified email at inf.ethz.ch - Homepage
Title
Cited by
Cited by
Year
Silq: A high-level quantum language with safe uncomputation and intuitive semantics
B Bichsel, M Baader, T Gehr, M Vechev
Proceedings of the 41st ACM SIGPLAN Conference on Programming Language …, 2020
1792020
Certifying geometric robustness of neural networks
M Balunović, M Baader, G Singh, T Gehr, M Vechev
Advances in Neural Information Processing Systems 32, 2019
1482019
Certified Defense to Image Transformations via Randomized Smoothing
M Fischer, M Baader, M Vechev
Advances in Neural Information Processing Systems 33, 2020
842020
Scalable Certified Segmentation via Randomized Smoothing
M Fischer, M Baader, M Vechev
International Conference on Machine Learning, 3340-3351, 2021
482021
Fast and precise certification of transformers
G Bonaert, DI Dimitrov, M Baader, M Vechev
Proceedings of the 42nd ACM SIGPLAN International Conference on Programming …, 2021
392021
Certified defenses: Why tighter relaxations may hurt training
N Jovanović, M Balunović, M Baader, M Vechev
Transactions on Machine Learning Research (TMLR 10/2022), 2022
34*2022
Universal approximation with certified networks
M Baader, M Mirman, M Vechev
International Conference on Learning Representations, 2020
322020
Efficient Certification of Spatial Robustness
A Ruoss, M Baader, M Balunović, M Vechev
AAAI Conference on Artificial Intelligence 35, 2021
302021
Latent space smoothing for individually fair representations
M Peychev, A Ruoss, M Balunović, M Baader, M Vechev
European Conference on Computer Vision, 535-554, 2022
182022
Evading Data Contamination Detection for Language Models is (too) Easy
J Dekoninck, MN Müller, M Baader, M Fischer, M Vechev
arXiv preprint arXiv:2402.02823, 2024
142024
The Fundamental Limits of Neural Networks for Interval Certified Robustness
MB Mirman, M Baader, M Vechev
Transactions on Machine Learning Research, 2022
13*2022
Abstraqt: Analysis of Quantum Circuits via Abstract Stabilizer Simulation
B Bichsel, A Paradis, M Baader, M Vechev
Quantum 7, 1185, 2023
102023
Certified Robustness to Data Poisoning in Gradient-Based Training
P Sosnin, MN Müller, M Baader, C Tsay, M Wicker
arXiv preprint arXiv:2406.05670, 2024
92024
Spear: Exact gradient inversion of batches in federated learning
DI Dimitrov, M Baader, M Müller, M Vechev
Advances in Neural Information Processing Systems 37, 106768-106799, 2024
62024
EXPRESSIVITY OF RELU-NETWORKS UNDER CONVEX RELAXATIONS
M Baader, MN Müller, Y Mao, M Vechev
arXiv preprint arXiv:2311.04015, 2023
62023
Ward: Provable RAG Dataset Inference via LLM Watermarks
N Jovanović, R Staab, M Baader, M Vechev
arXiv preprint arXiv:2410.03537, 2024
42024
Dager: Exact gradient inversion for large language models
I Petrov, DI Dimitrov, M Baader, M Müller, M Vechev
Advances in Neural Information Processing Systems 37, 87801-87830, 2024
12024
A Unified Approach to Routing and Cascading for LLMs
J Dekoninck, M Baader, M Vechev
arXiv preprint arXiv:2410.10347, 2024
12024
ToolFuzz--Automated Agent Tool Testing
I Milev, M Balunović, M Baader, M Vechev
arXiv preprint arXiv:2503.04479, 2025
2025
BaxBench: Can LLMs Generate Correct and Secure Backends?
M Vero, N Mündler, V Chibotaru, V Raychev, M Baader, N Jovanović, ...
arXiv preprint arXiv:2502.11844, 2025
2025
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