Mark Niklas Müller

About me

Mark N. Müller is a senior Ph.D. student at the Secure, Reliable, and Intelligent Systems Lab of ETH Zürich and advised by Prof. Martin Vechev. Mark’s research focuses on provable guarantees for machine learning models. This includes both deterministic and probabilistic certification methods for a diverse range of architectures and certified training methods. He has led the research on these topics at SRI Lab for the last two years, including two industry collaborations, co-organized the Verification of Neural Networks Competition 2022 (VNN-Comp’22), and was the lead organizer for the 2nd Workshop on Formal Verification of Machine Learning (WFVML'23) at ICML’23 this year.

Education

ETH Zurich, September 2019 - October 2020
Visiting Student in the Department of Computer Science
University of Stuttgart, October 2018 - October 2020
M.Sc. in Aerospace Engineering

CC BY 4.0 by @fontawesome - https://fontawesome.com

Best Master's degree in Aerospace Engineering

University of Stuttgart, October 2014 - April 2018
B.Sc. in Aerospace Engineering

Best Bachelor's degree in Aerospace Engineering

Publications

2024

Understanding Certified Training with Interval Bound Propagation

Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin Vechev

ICLR 2024

Paper

Code

Expressivity of ReLU-Networks under Convex Relaxations

Maximilian Baader*, Mark Niklas Müller*, Yuhao Mao, Martin Vechev

ICLR 2024 * Equal contribution

Paper

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

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

arXiv 2024

Paper

Code

2023

Automated Classification of Model Errors on ImageNet

Momchil Peychev*, Mark Niklas Müller*, Marc Fischer, Martin Vechev

NeurIPS 2023 * Equal contribution

Poster

Slides

Talk

Paper

Code

Connecting Certified and Adversarial Training

Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin Vechev

NeuIPS 2023

Paper

Code

Prompt Sketching for Large Language Models

Luca Beurer-Kellner, Mark Niklas Müller, Marc Fischer, Martin Vechev

arXiv 2023

Paper

Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks

Mark Niklas Müller, Marc Fischer, Robin Staab, Martin Vechev

PLDI 2023

Paper

Efficient Certified Training and Robustness Verification of Neural ODEs

Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin Vechev

ICLR 2023

Paper

Code

Certified Training: Small Boxes are All You Need

Mark Niklas Müller*, Franziska Eckert*, Marc Fischer, Martin Vechev

ICLR 2023 * Equal contribution CC BY 4.0 by @fontawesome - https://fontawesome.com

Spotlight

Slides

Paper

Code

First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)

Christopher Brix, Mark Niklas Müller, Stanley Bak, Changliu Liu, Taylor T. Johnson

STTT ExPLAIn 2023

Paper

2022

The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Results

Mark Niklas Müller*, Christopher Brix*, Stanley Bak, Changliu Liu, Taylor T. Johnson

arXiv 2022 * Equal contribution

Paper

(De-)Randomized Smoothing for Decision Stump Ensembles

Miklós Z. Horváth*, Mark Niklas Müller*, Marc Fischer, Martin Vechev

NeurIPS 2022 * Equal contribution

Paper

Code

Robust and Accurate - Compositional Architectures for Randomized Smoothing

Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin Vechev

SRML@ICLR 2022

Paper

Code

Boosting Randomized Smoothing with Variance Reduced Classifiers

Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin Vechev

ICLR 2022 CC BY 4.0 by @fontawesome - https://fontawesome.com

Spotlight

Blog

Paper

Code

Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound

Claudio Ferrari, Mark Niklas Müller, Nikola Jovanović, Martin Vechev

ICLR 2022

Blog

Paper

Code

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

Mark Niklas Müller*, Gleb Makarchuk*, Gagandeep Singh, Markus Püschel, Martin Vechev

POPL 2022 * Equal contribution

Slides

Talk

Paper

2021

Certify or Predict: Boosting Certified Robustness with Compositional Architectures

Mark Niklas Müller, Mislav Balunović, Martin Vechev

ICLR 2021

Slides

Talk

Paper

Invited Talks and Academic Service

Head Organizer of the 2nd Workshop on Formal Verification of Machine Learning WFVML'23@ICML (Honolulu, United States)
Invited Talk at MobiliT.Ai 2023 - Realistic Neural Networks with Robustness Guarantees (Toulouse, France)
Invited Talk at RPL@KTH - Realistic Neural Networks with Guarantees (Stockholm, Sweden)
Invited Talk at LMML@FLoC - Verification of Realistic Neural Networks (Haifa, Israel)
Co-Organizer of the 3rd International Verification of Neural Networks Competition, VNN-COMP'22 (Haifa, Israel)
Invited Talk at the Workshop on Robust Artificial Intelligence - Scalable and Precise Certification of Neural Networks (online)
Reviewer for: JMLR, NeurIPS'23, ICLR'24, TSRML@NeurIPS'22, WFVML@ICML'22

Supervised students

M.Sc. - Simone Barbaro, Out of Distribution Detection via Calibrated Confidence
M.Sc. - Claudio Ferrari, Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound [ICLR'2022]
RCS - Ahmed Bouhoula, Branching Strategies for Multi-Neuron-Constraint-Based Bounding
RCS - Miklós Z. Horváth, Boosting Randomized Smoothing with Variance Reduced Classifiers [ICLR'2022 (Spotlight)]
RCS - Miklós Z. Horváth, Robust and Accurate -- Compositional Architectures for Randomized Smoothings [SRML@ICLR'2022]
M.Sc. - Miklós Z. Horváth, (De-)Randomized Smoothing for Decision Stump Ensembles [NeurIPS'2022]
M.Sc. - Franziska Eckert, Certified Training: Small Boxes are All You Need [ICLR'2023 (Spotlight)]
M.Sc. - Mustafa Zeqiri, Efficient Robustness Verification of Neural Ordinary Differential Equations [ICLR'2023]
PW - Yuhao Mao, Connecting Certified and Adversarial Training [NeurIPS'2023]
M.Sc. - Robert Szasz, Focusing on Important Samples in Certified Training
M.Sc. - Abra Ganz, Fine-tuning for Randomised Smoothing
M.Sc. - Yuhao Mao, Understanding Certified Training with Interval Bound Propagation [arXiv]

Teaching

Reliable and Trustworthy Artificial Intelligence - Fall 2023 - Course TA
Rigorous Software Engineering - Spring 2023 - Head TA
Deep Learning for Big Code - Spring 2023 - Course TA
Reliable and Trustworthy Artificial Intelligence - Fall 2022 - Course TA
Rigorous Software Engineering - Spring 2022 - Course TA
Deep Learning for Big Code - Spring 2022 - Course TA
Reliable and Trustworthy Artificial Intelligence - Fall 2021 - Course TA
Rigorous Software Engineering - Spring 2021 - Course TA
Deep Learning for Big Code - Spring 2021 - Course TA

Work experience

G-Research, London, UK, 06/2023 - 09/2023
Quantitative Research Summer Intern
Dr. Ing. h.c. F. Porsche AG, Weissach, DE, 11/2018 - 08/2019
Working Student
Bosch Rexroth AG, Stuttgart, DE, 09/2018 - 10/2018
Data Science Intern
Mercedes-AMG Petronas Formula One Team, Brackley, UK, 07/2017 - 07/2018
Industrial Placement - Aerodynamicist

Awards

LRT Award for the best overall Master degree in Aerospace Engineering
AIRBUS Defence & Space Award for the best overall Bachelor degree in Aerospace Engineering