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ORCIDMartin T. Vechev
- > Home > Persons > Martin T. Vechev
Publications
- 2024
[i67]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)- [i62]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) - [i60]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) - 2023
- [j16]Benjamin Bichsel, Anouk Paradis, Maximilian Baader, Martin T. Vechev:
Abstraqt: Analysis of Quantum Circuits via Abstract Stabilizer Simulation. Quantum 7: 1185 (2023) - [i57]Benjamin Bichsel, Maximilian Baader, Anouk Paradis, Martin T. Vechev:
Abstraqt: Analysis of Quantum Circuits via Abstract Stabilizer Simulation. CoRR abs/2304.00921 (2023) - [i49]Maximilian Baader, Mark Niklas Müller, Yuhao Mao, Martin T. Vechev:
Expressivity of ReLU-Networks under Convex Relaxations. CoRR abs/2311.04015 (2023) - 2022
- [j13]Nikola Jovanovic, Mislav Balunovic, Maximilian Baader, Martin T. Vechev:
On the Paradox of Certified Training. Trans. Mach. Learn. Res. 2022 (2022) - [j12]Matthew Mirman, Maximilian Baader, Martin T. Vechev:
The Fundamental Limits of Neural Networks for Interval Certified Robustness. Trans. Mach. Learn. Res. 2022 (2022) - [c134]Momchil Peychev
, Anian Ruoss, Mislav Balunovic, Maximilian Baader, Martin T. Vechev:
Latent Space Smoothing for Individually Fair Representations. ECCV (13) 2022: 535-554 - 2021
- [c122]Anian Ruoss, Maximilian Baader, Mislav Balunovic, Martin T. Vechev:
Efficient Certification of Spatial Robustness. AAAI 2021: 2504-2513 - [c116]Marc Fischer, Maximilian Baader, Martin T. Vechev:
Scalable Certified Segmentation via Randomized Smoothing. ICML 2021: 3340-3351 - [c109]Gregory Bonaert, Dimitar I. Dimitrov, Maximilian Baader, Martin T. Vechev:
Fast and precise certification of transformers. PLDI 2021: 466-481 - [i32]Nikola Jovanovic, Mislav Balunovic, Maximilian Baader, Martin T. Vechev:
Certified Defenses: Why Tighter Relaxations May Hurt Training? CoRR abs/2102.06700 (2021) - [i26]Marc Fischer, Maximilian Baader, Martin T. Vechev:
Scalable Certified Segmentation via Randomized Smoothing. CoRR abs/2107.00228 (2021) - [i22]Momchil Peychev, Anian Ruoss, Mislav Balunovic, Maximilian Baader, Martin T. Vechev:
Latent Space Smoothing for Individually Fair Representations. CoRR abs/2111.13650 (2021) - [i21]Matthew Mirman, Maximilian Baader, Martin T. Vechev:
The Fundamental Limits of Interval Arithmetic for Neural Networks. CoRR abs/2112.05235 (2021) - 2020
- [c106]Maximilian Baader, Matthew Mirman, Martin T. Vechev:
Universal Approximation with Certified Networks. ICLR 2020 - [c101]Marc Fischer, Maximilian Baader, Martin T. Vechev:
Certified Defense to Image Transformations via Randomized Smoothing. NeurIPS 2020 - [c98]Benjamin Bichsel, Maximilian Baader, Timon Gehr, Martin T. Vechev:
Silq: a high-level quantum language with safe uncomputation and intuitive semantics. PLDI 2020: 286-300 - [i18]Marc Fischer, Maximilian Baader, Martin T. Vechev:
Certification of Semantic Perturbations via Randomized Smoothing. CoRR abs/2002.12463 (2020) - [i11]Anian Ruoss, Maximilian Baader, Mislav Balunovic, Martin T. Vechev:
Efficient Certification of Spatial Robustness. CoRR abs/2009.09318 (2020) - 2019
- [c87]Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin T. Vechev:
Certifying Geometric Robustness of Neural Networks. NeurIPS 2019: 15287-15297 - [i9]Maximilian Baader, Matthew Mirman, Martin T. Vechev:
Universal Approximation with Certified Networks. CoRR abs/1909.13846 (2019)
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