About me
I am Marc Fischer, a PhD student at the Department of Computer Science, ETH Zürich. I am part of the Secure, Reliable, and Intelligent Systems Lab, supervised by Martin Vechev, since March 2019.
Education
ETH Zurich, September 2013 – February 2019: B.Sc. and M.Sc. in Computer Science
Awards
| 2019 | ETH Medal for Outstanding Master Thesis | Awarded |
Publications
2024
Prompt Sketching for Large Language Models
Luca Beurer-Kellner, Mark Niklas Müller, Marc Fischer, Martin Vechev
ICML
2024
Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation
Luca Beurer-Kellner, Marc Fischer, Martin Vechev
ICML
2024
Understanding Certified Training with Interval Bound Propagation
Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin Vechev
ICLR
2024
Controlled Text Generation via Language Model Arithmetic
Jasper Dekoninck, Marc Fischer, Luca Beurer-Kellner, Martin Vechev
ICLR
2024
Spotlight
Spotlight
Overcoming the Paradox of Certified Training with Gaussian Smoothing
Stefan Balauca, Mark Niklas Müller, Yuhao Mao, Maximilian Baader, Marc Fischer, Martin Vechev
arXiv
2024
Evading Data Contamination Detection for Language Models is (too) Easy
Jasper Dekoninck, Mark Niklas Müller, Maximilian Baader, Marc Fischer, Martin Vechev
arXiv
2024
2023
Automated Classification of Model Errors on ImageNet
Momchil Peychev*, Mark Niklas Müller*, Marc Fischer, Martin Vechev
NeurIPS
2023
* Equal contribution
Connecting Certified and Adversarial Training
Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin Vechev
NeuIPS
2023
LMQL Chat: Scripted Chatbot Development
Luca Beurer-Kellner*, Marc Fischer*, Martin Vechev
Neural Conversational AI Workshop, TEACH -- ICML
2023
* Equal contribution
Large Language Models are Zero-Shot Multi-Tool Users
Luca Beurer-Kellner*, Marc Fischer*, Martin Vechev
Knowlege and Logical Reasoning Workshop -- ICML
2023
* Equal contribution
Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks
Mark Niklas Müller, Marc Fischer, Robin Staab, Martin Vechev
PLDI
2023
Prompting Is Programming: A Query Language for Large Language Models
Luca Beurer-Kellner, Marc Fischer, Martin Vechev
PLDI
2023
Efficient Certified Training and Robustness Verification of Neural ODEs
Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin Vechev
ICLR
2023
Certified Training: Small Boxes are All You Need
Mark Niklas Müller*, Franziska Eckert*, Marc Fischer, Martin Vechev
ICLR
2023
* Equal contribution
Spotlight
Spotlight
2022
(De-)Randomized Smoothing for Decision Stump Ensembles
Miklós Z. Horváth*, Mark Niklas Müller*, Marc Fischer, Martin Vechev
NeurIPS
2022
* Equal contribution
Private and Reliable Neural Network Inference
Nikola Jovanović, Marc Fischer, Samuel Steffen, Martin Vechev
ACM CCS
2022
Shared Certificates for Neural Network Verification
Marc Fischer*, Christian Sprecher*, Dimitar I. Dimitrov, Gagandeep Singh, Martin Vechev
CAV
2022
* Equal contribution
Robust and Accurate - Compositional Architectures for Randomized Smoothing
Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin Vechev
SRML@ICLR
2022
Boosting Randomized Smoothing with Variance Reduced Classifiers
Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin Vechev
ICLR
2022
Spotlight
Spotlight
2021
Scalable Certified Segmentation via Randomized Smoothing
Marc Fischer, Maximilian Baader, Martin Vechev
ICML
2021
2020
Learning Certified Individually Fair Representations
Anian Ruoss, Mislav Balunović, Marc Fischer, Martin Vechev
NeurIPS
2020
Certified Defense to Image Transformations via Randomized Smoothing
Marc Fischer, Maximilian Baader, Martin Vechev
NeurIPS
2020
2019
Online Robustness Training for Deep Reinforcement Learning
Marc Fischer, Matthew Mirman, Steven Stalder, Martin Vechev
arXiv
2019
DL2: Training and Querying Neural Networks with Logic
Marc Fischer, Mislav Balunović, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin Vechev
ICML
2019
2018
Robust Relational Layouts Synthesis from Examples for Android
Pavol Bielik, Marc Fischer, Martin Vechev
ACM OOPSLA
2018