Robust relational layout synthesis from examples for Android. - Pavol Bielik; Marc Fischer 002; Martin T. Vechev (Proc. ACM Program. Lang., 2018)

DL2: Training and Querying Neural Networks with Logic. - Marc Fischer 002; Mislav Balunovic; Dana Drachsler-Cohen; Timon Gehr; Ce Zhang 001; Martin T. Vechev (ICML, 2019)

Online Robustness Training for Deep Reinforcement Learning. - Marc Fischer 002; Matthew Mirman; Steven Stalder; Martin T. Vechev (CoRR, 2019)

Certified Defense to Image Transformations via Randomized Smoothing. - Marc Fischer 002; Maximilian Baader; Martin T. Vechev (NeurIPS, 2020)

Learning Certified Individually Fair Representations. - Anian Ruoss; Mislav Balunovic; Marc Fischer 002; Martin T. Vechev (NeurIPS, 2020)

Certification of Semantic Perturbations via Randomized Smoothing. - Marc Fischer 002; Maximilian Baader; Martin T. Vechev (CoRR, 2020)

Scalable Certified Segmentation via Randomized Smoothing. - Marc Fischer 002; Maximilian Baader; Martin T. Vechev (ICML, 2021)

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

Shared Certificates for Neural Network Verification. - Marc Fischer 002; Christian Sprecher; Dimitar I. Dimitrov; Gagandeep Singh 001; Martin T. Vechev (CAV (1), 2022)

Private and Reliable Neural Network Inference. - Nikola Jovanovic 001; Marc Fischer 002; Samuel Steffen; Martin T. Vechev (CCS, 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)

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)

Prompting Is Programming: A Query Language for Large Language Models. - Luca Beurer-Kellner; Marc Fischer 002; Martin T. Vechev (Proc. ACM Program. Lang., 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)

Controlled Text Generation via Language Model Arithmetic. - Jasper Dekoninck; Marc Fischer 002; Luca Beurer-Kellner; 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)

Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation. - Luca Beurer-Kellner; Marc Fischer 002; Martin T. Vechev (ICML, 2024)

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

AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. - Edoardo Debenedetti; Jie Zhang 107; Mislav Balunovic; Luca Beurer-Kellner; Marc Fischer 002; Florian Tramèr (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)

AgentDojo: A Dynamic Environment to Evaluate Attacks and Defenses for LLM Agents. - Edoardo Debenedetti; Jie Zhang 107; Mislav Balunovic; Luca Beurer-Kellner; Marc Fischer 002; Florian Tramèr (CoRR, 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)

Extending Randomized Smoothing to New Application Domains. - Marc Fischer 002 (2025)

Design Patterns for Securing LLM Agents against Prompt Injections. - Luca Beurer-Kellner; Beat Buesser; Ana-Maria Cretu 001; Edoardo Debenedetti; Daniel Dobos; Daniel Fabian; Marc Fischer 002; David Froelicher; Kathrin Grosse; Daniel Naeff; Ezinwanne Ozoani; Andrew Paverd; et al. (CoRR, 2025)