Maximilian Baader

According to our database, Maximilian Baader authored at least 28 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

ToolFuzz - Automated Agent Tool Testing.

Ivan Milev, Mislav Balunovic, Maximilian Baader, Martin T. Vechev

CoRR, March, 2025

BaxBench: Can LLMs Generate Correct and Secure Backends?

Mark Vero, Niels Mündler, Victor Chibotaru, Veselin Raychev, Maximilian Baader, Nikola Jovanovic, Jingxuan He, Martin T. Vechev

CoRR, February, 2025

Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations.

Stefan Balauca, Mark Niklas Müller, Yuhao Mao, Maximilian Baader, Marc Fischer, Martin T. Vechev

Trans. Mach. Learn. Res., 2025

GRAIN: Exact Graph Reconstruction from Gradients.

Maria Drencheva, Ivo Petrov, Maximilian Baader, Dimitar Iliev Dimitrov, Martin T. Vechev

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation.

Jasper Dekoninck, Maximilian Baader, Martin T. Vechev

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Ward: Provable RAG Dataset Inference via LLM Watermarks.

Nikola Jovanovic, Robin Staab, Maximilian Baader, Martin T. Vechev

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Expressivity of Certified Neural Networks.

Maximilian Baader

PhD thesis, 2024

A Unified Approach to Routing and Cascading for LLMs.

Jasper Dekoninck, Maximilian Baader, Martin T. Vechev

CoRR, 2024

Certified Robustness to Data Poisoning in Gradient-Based Training.

Philip Sosnin, Mark Niklas Müller, Maximilian Baader, Calvin Tsay, Matthew Wicker

CoRR, 2024

Overcoming the Paradox of Certified Training with Gaussian Smoothing.

Stefan Balauca, Mark Niklas Müller, Yuhao Mao, Maximilian Baader, Marc Fischer, Martin T. Vechev

CoRR, 2024

Evading Data Contamination Detection for Language Models is (too) Easy.

Jasper Dekoninck, Mark Niklas Müller, Maximilian Baader, Marc Fischer, Martin T. Vechev

CoRR, 2024

DAGER: Exact Gradient Inversion for Large Language Models.

Ivo Petrov, Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

SPEAR: Exact Gradient Inversion of Batches in Federated Learning.

Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Expressivity of ReLU-Networks under Convex Relaxations.

Maximilian Baader, Mark Niklas Müller, Yuhao Mao, Martin T. Vechev

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Abstraqt: Analysis of Quantum Circuits via Abstract Stabilizer Simulation.

Benjamin Bichsel, Anouk Paradis, Maximilian Baader, Martin T. Vechev

Quantum, November, 2023

2022

The Fundamental Limits of Neural Networks for Interval Certified Robustness.

Matthew Mirman, Maximilian Baader, Martin T. Vechev

Trans. Mach. Learn. Res., 2022

On the Paradox of Certified Training.

Nikola Jovanovic, Mislav Balunovic, Maximilian Baader, Martin T. Vechev

Trans. Mach. Learn. Res., 2022

Latent Space Smoothing for Individually Fair Representations.

Momchil Peychev, Anian Ruoss, Mislav Balunovic, Maximilian Baader, Martin T. Vechev

Proceedings of the Computer Vision - ECCV 2022, 2022

2021

The Fundamental Limits of Interval Arithmetic for Neural Networks.

Matthew Mirman, Maximilian Baader, Martin T. Vechev

CoRR, 2021

Certified Defenses: Why Tighter Relaxations May Hurt Training?

Nikola Jovanovic, Mislav Balunovic, Maximilian Baader, Martin T. Vechev

CoRR, 2021

Fast and precise certification of transformers.

Gregory Bonaert, Dimitar I. Dimitrov, Maximilian Baader, Martin T. Vechev

Proceedings of the PLDI '21: 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2021

Scalable Certified Segmentation via Randomized Smoothing.

Marc Fischer, Maximilian Baader, Martin T. Vechev

Proceedings of the 38th International Conference on Machine Learning, 2021

Efficient Certification of Spatial Robustness.

Anian Ruoss, Maximilian Baader, Mislav Balunovic, Martin T. Vechev

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Certification of Semantic Perturbations via Randomized Smoothing.

Marc Fischer, Maximilian Baader, Martin T. Vechev

CoRR, 2020

Silq: a high-level quantum language with safe uncomputation and intuitive semantics.

Benjamin Bichsel, Maximilian Baader, Timon Gehr, Martin T. Vechev

Proceedings of the 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2020

Certified Defense to Image Transformations via Randomized Smoothing.

Marc Fischer, Maximilian Baader, Martin T. Vechev

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Universal Approximation with Certified Networks.

Maximilian Baader, Matthew Mirman, Martin T. Vechev

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Certifying Geometric Robustness of Neural Networks.

Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin T. Vechev

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019