Search Results - Mark Niklas Müller

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  1. 1
    PRIMA: general and precise neural network certification via scalable convex hull approximations

    PRIMA: general and precise neural network certification via scalable convex hull approximations by Müller, Mark Niklas, Makarchuk, Gleb, Singh, Gagandeep, Püschel, Markus, Vechev, Martin

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  2. 2
    First three years of the international verification of neural networks competition (VNN-COMP)

    First three years of the international verification of neural networks competition (VNN-COMP) by Brix, Christopher, Müller, Mark Niklas, Bak, Stanley, Johnson, Taylor T., Liu, Changliu

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  3. 3
    Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks

    Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks by Mark Niklas Müller, Fischer, Marc, Staab, Robin, Vechev, Martin

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  4. 4
    Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks

    Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks by Müller, Mark Niklas, Fischer, Marc, Staab, Robin, Vechev, Martin

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  5. 5
    PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations

    PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations by Mark Niklas Müller, Makarchuk, Gleb, Singh, Gagandeep, Püschel, Markus, Vechev, Martin

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  6. 6
    SPEAR:Exact Gradient Inversion of Batches in Federated Learning

    SPEAR:Exact Gradient Inversion of Batches in Federated Learning by Dimitrov, Dimitar I, Baader, Maximilian, Mark Niklas Müller, Vechev, Martin

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  7. 7
    SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents

    SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents by Mündler, Niels, Mark Niklas Müller, He, Jingxuan, Vechev, Martin

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  8. 8
    Average Certified Radius is a Poor Metric for Randomized Smoothing

    Average Certified Radius is a Poor Metric for Randomized Smoothing by Sun, Chenhao, Mao, Yuhao, Mark Niklas Müller, Vechev, Martin

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  9. 9
    Understanding Certified Training with Interval Bound Propagation

    Understanding Certified Training with Interval Bound Propagation by Mao, Yuhao, Mark Niklas Müller, Fischer, Marc, Vechev, Martin

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  10. 10
    Prompt Sketching for Large Language Models

    Prompt Sketching for Large Language Models by Beurer-Kellner, Luca, Mark Niklas Müller, Fischer, Marc, Vechev, Martin

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  11. 11
    Expressivity of ReLU-Networks under Convex Relaxations

    Expressivity of ReLU-Networks under Convex Relaxations by Baader, Maximilian, Mark Niklas Müller, Mao, Yuhao, Vechev, Martin

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  12. 12
    TAPS: Connecting Certified and Adversarial Training

    TAPS: Connecting Certified and Adversarial Training by Mao, Yuhao, Mark Niklas Müller, Fischer, Marc, Vechev, Martin

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  13. 13
    Certified Training: Small Boxes are All You Need

    Certified Training: Small Boxes are All You Need by Mark Niklas Müller, Eckert, Franziska, Fischer, Marc, Vechev, Martin

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  14. 14
    Efficient Certified Training and Robustness Verification of Neural ODEs

    Efficient Certified Training and Robustness Verification of Neural ODEs by Zeqiri, Mustafa, Mark Niklas Müller, Fischer, Marc, Vechev, Martin

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  15. 15
    DAGER: Exact Gradient Inversion for Large Language Models

    DAGER: Exact Gradient Inversion for Large Language Models by Petrov, Ivo, Dimitrov, Dimitar I, Baader, Maximilian, Mark Niklas Müller, Vechev, Martin

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  16. 16
    Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations

    Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations by Balauca, Stefan, Mark Niklas Müller, Mao, Yuhao, Baader, Maximilian, Fischer, Marc, Vechev, Martin

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  17. 17
    Diagnosing Robotics Systems Issues with Large Language Models

    Diagnosing Robotics Systems Issues with Large Language Models by Herrmann, Jordis Emilia, Gopinath, Aswath Mandakath, Norrlof, Mikael, Mark Niklas Müller

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  18. 18
    (De-)Randomized Smoothing for Decision Stump Ensembles

    (De-)Randomized Smoothing for Decision Stump Ensembles by Horváth, Miklós Z, Mark Niklas Müller, Fischer, Marc, Vechev, Martin

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  19. 19
    Mitigating Catastrophic Forgetting in Language Transfer via Model Merging

    Mitigating Catastrophic Forgetting in Language Transfer via Model Merging by Alexandrov, Anton, Raychev, Veselin, Mark Niklas Müller, Zhang, Ce, Vechev, Martin, Toutanova, Kristina

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  20. 20
    Evading Data Contamination Detection for Language Models is (too) Easy

    Evading Data Contamination Detection for Language Models is (too) Easy by Dekoninck, Jasper, Mark Niklas Müller, Baader, Maximilian, Fischer, Marc, Vechev, Martin

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