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ORCIDMartin T. Vechev
- > Home > Persons > Martin T. Vechev
Publications
- 2024
[i68]Momchil Peychev, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Automated Classification of Model Errors on ImageNet. CoRR abs/2401.02430 (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) - [i61]Luca Beurer-Kellner, Marc Fischer, Martin T. Vechev:
Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation. CoRR abs/2403.06988 (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
- [j18]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) - [j17]Luca Beurer-Kellner, Marc Fischer, Martin T. Vechev:
Prompting Is Programming: A Query Language for Large Language Models. Proc. ACM Program. Lang. 7(PLDI): 1946-1969 (2023) - [c144]Mark Niklas Müller, Franziska Eckert, Marc Fischer, Martin T. Vechev:
Certified Training: Small Boxes are All You Need. ICLR 2023 - [c143]Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Efficient Certified Training and Robustness Verification of Neural ODEs. ICLR 2023 - [c139]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Connecting Certified and Adversarial Training. NeurIPS 2023 - [c138]Momchil Peychev, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Automated Classification of Model Errors on ImageNet. NeurIPS 2023 - [i58]Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Efficient Certified Training and Robustness Verification of Neural ODEs. CoRR abs/2303.05246 (2023) - [i56]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
TAPS: Connecting Certified and Adversarial Training. CoRR abs/2305.04574 (2023) - [i52]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Understanding Certified Training with Interval Bound Propagation. CoRR abs/2306.10426 (2023) - [i48]Luca Beurer-Kellner, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Prompt Sketching for Large Language Models. CoRR abs/2311.04954 (2023) - [i46]Jasper Dekoninck, Marc Fischer, Luca Beurer-Kellner, Martin T. Vechev:
Controlled Text Generation via Language Model Arithmetic. CoRR abs/2311.14479 (2023) - 2022
- [c137]Marc Fischer, Christian Sprecher, Dimitar I. Dimitrov, Gagandeep Singh, Martin T. Vechev:
Shared Certificates for Neural Network Verification. CAV (1) 2022: 127-148 - [c136]Nikola Jovanovic, Marc Fischer, Samuel Steffen, Martin T. Vechev:
Private and Reliable Neural Network Inference. CCS 2022: 1663-1677 - [c129]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Boosting Randomized Smoothing with Variance Reduced Classifiers. ICLR 2022 - [c125]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
(De-)Randomized Smoothing for Decision Stump Ensembles. NeurIPS 2022 - [i44]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Robust and Accurate - Compositional Architectures for Randomized Smoothing. CoRR abs/2204.00487 (2022) - [i41]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
(De-)Randomized Smoothing for Decision Stump Ensembles. CoRR abs/2205.13909 (2022) - [i38]Mark Niklas Müller, Franziska Eckert, Marc Fischer, Martin T. Vechev:
Certified Training: Small Boxes are All You Need. CoRR abs/2210.04871 (2022) - [i36]Nikola Jovanovic, Marc Fischer, Samuel Steffen, Martin T. Vechev:
Private and Reliable Neural Network Inference. CoRR abs/2210.15614 (2022) - [i34]Luca Beurer-Kellner, Marc Fischer, Martin T. Vechev:
Prompting Is Programming: A Query Language For Large Language Models. CoRR abs/2212.06094 (2022) - 2021
- [c116]Marc Fischer, Maximilian Baader, Martin T. Vechev:
Scalable Certified Segmentation via Randomized Smoothing. ICML 2021: 3340-3351 - [i27]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Boosting Randomized Smoothing with Variance Reduced Classifiers. CoRR abs/2106.06946 (2021) - [i26]Marc Fischer, Maximilian Baader, Martin T. Vechev:
Scalable Certified Segmentation via Randomized Smoothing. CoRR abs/2107.00228 (2021) - [i25]Christian Sprecher, Marc Fischer, Dimitar I. Dimitrov, Gagandeep Singh, Martin T. Vechev:
Shared Certificates for Neural Network Verification. CoRR abs/2109.00542 (2021) - [i24]Mark Niklas Müller, Robin Staab, Marc Fischer, Martin T. Vechev:
Effective Certification of Monotone Deep Equilibrium Models. CoRR abs/2110.08260 (2021) - 2020
- [c101]Marc Fischer, Maximilian Baader, Martin T. Vechev:
Certified Defense to Image Transformations via Randomized Smoothing. NeurIPS 2020 - [c100]Anian Ruoss, Mislav Balunovic, Marc Fischer, Martin T. Vechev:
Learning Certified Individually Fair Representations. NeurIPS 2020 - [i19]Anian Ruoss, Mislav Balunovic, Marc Fischer, Martin T. Vechev:
Learning Certified Individually Fair Representations. CoRR abs/2002.10312 (2020) - [i18]Marc Fischer, Maximilian Baader, Martin T. Vechev:
Certification of Semantic Perturbations via Randomized Smoothing. CoRR abs/2002.12463 (2020) - 2019
- [c89]Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin T. Vechev:
DL2: Training and Querying Neural Networks with Logic. ICML 2019: 1931-1941 - [i8]Marc Fischer, Matthew Mirman, Steven Stalder, Martin T. Vechev:
Online Robustness Training for Deep Reinforcement Learning. CoRR abs/1911.00887 (2019) - 2018
- [j9]Pavol Bielik, Marc Fischer, Martin T. Vechev:
Robust relational layout synthesis from examples for Android. Proc. ACM Program. Lang. 2(OOPSLA): 156:1-156:29 (2018)
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last updated on 2024-04-07 22:27 CEST by the dblp team
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