STARTUPS
The mission of our ETH spin-off LatticeFlow is to enable organizations to deliver safe, robust, and reliable AI systems. To this end, LatticeFlow collaborates with leading enterprises such as Swiss Federal Railways (SBB) and Siemens, and government agencies such as the US Army and Germany's Federal Office of Information Security (BSI).
You can read more about our vision and product on TechCrunch and ETH News.
Publications
Shared Certificates for Neural Network Verification,
CAV 2022
Marc Fischer*, Christian Sprecher*, Dimitar I. Dimitrov, Gagandeep Singh, Martin Vechev
Data Leakage in Federated Averaging,
arXiv 2022
Dimitar I. Dimitrov, Mislav Balunović, Nikola Konstantinov, Martin Vechev
(De-)Randomized Smoothing for Decision Stump Ensembles,
arXiv 2022
Miklós Z. Horváth*, Mark Niklas Müller*, Marc Fischer, Martin Vechev
Robust and Accurate - Compositional Architectures for Randomized Smoothing,
SRML@ICLR 2022
Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin Vechev
Boosting Randomized Smoothing with Variance Reduced Classifiers,
ICLR (Spotlight) 2022
Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin Vechev
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound,
ICLR 2022
Claudio Ferrari, Mark Niklas Müller, Nikola Jovanović, Martin Vechev
Provably Robust Adversarial Examples,
ICLR 2022
Dimitar I. Dimitrov, Gagandeep Singh, Timon Gehr, Martin Vechev
Fair Normalizing Flows,
ICLR 2022
Mislav Balunović, Anian Ruoss, Martin Vechev
Bayesian Framework for Gradient Leakage,
ICLR 2022
Mislav Balunović, Dimitar I. Dimitrov, Robin Staab, Martin Vechev
LAMP: Extracting Text from Gradients with Language Model Priors,
arXiv 2022
Dimitar I. Dimitrov*, Mislav Balunović*, Nikola Jovanović, Martin Vechev
PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations,
POPL 2022
Mark Niklas Müller, Gleb Makarchuk, Gagandeep Singh, Markus Püschel, Martin Vechev
The Fundamental Limits of Interval Arithmetic for Neural Networks,
arXiv 2021
Matthew Mirman, Maximilian Baader, Martin Vechev
Automated Discovery of Adaptive Attacks on Adversarial Defenses,
NeurIPS 2021
Chengyuan Yao, Pavol Bielik, Petar Tsankov, Martin Vechev
Bayesian Framework for Gradient Leakage,
NeurIPS Workshop on New Frontiers in Federated Learning 2021
Mislav Balunovic, Dimitar I. Dimitrov, Robin Staab, Martin Vechev
Robustness Certification for Point Cloud Models,
ICCV 2021
Tobias Lorenz, Anian Ruoss, Mislav Balunovic, Gagandeep Singh, Martin Vechev
Effective Certification of Monotone Deep Equilibrium Models,
arXiv 2021
Mark Niklas Müller, Robin Staab, Marc Fischer, Martin Vechev
Scalable Polyhedral Verification of Recurrent Neural Networks,
CAV 2021
Wonryong Ryou, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Dan, Martin Vechev
Automated Discovery of Adaptive Attacks on Adversarial Defenses,
AutoML@ICML (Oral) 2021
Chengyuan Yao, Pavol Bielik, Petar Tsankov, Martin Vechev
Scalable Certified Segmentation via Randomized Smoothing,
ICML 2021
Marc Fischer, Maximilian Baader, Martin Vechev
Fair Normalizing Flows,
arXiv 2021
Mislav Balunovic, Anian Ruoss, Martin Vechev
Fast and Precise Certification of Transformers,
PLDI 2021
Gregory Bonaert, Dimitar I. Dimitrov, Maximilian Baader, Martin Vechev
Certified Defenses: Why Tighter Relaxations May Hurt Training,
arXiv 2021
Nikola Jovanović*, Mislav Balunović*, Maximilian Baader, Martin Vechev
Boosting Randomized Smoothing with Variance Reduced Classifiers,
arXiv 2021
Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin Vechev
Certify or Predict: Boosting Certified Robustness with Compositional Architectures,
ICLR 2021
Mark Niklas Müller, Mislav Balunovic, Martin Vechev
Robustness Certification with Generative Models,
PLDI 2021
Matthew Mirman, Alexander Hägele, Timon Gehr, Pavol Bielik, Martin Vechev
Scaling Polyhedral Neural Network Verification on GPUs,
MLSys 2021
Christoph Müller*, François Serre*, Gagandeep Singh, Markus Püschel, Martin Vechev
Efficient Certification of Spatial Robustness,
AAAI 2021
Anian Ruoss, Maximilian Baader, Mislav Balunovic, Martin Vechev
Scaling Polyhedral Neural Network Verification on GPUs,
MLSys 2021
Christoph Müller, Francois Serre, Gagandeep Singh, Markus Püschel, Martin Vechev
Learning Certified Individually Fair Representations,
NeurIPS 2020
Anian Ruoss, Mislav Balunovic, Marc Fischer, Martin Vechev
Certified Defense to Image Transformations via Randomized Smoothing,
NeurIPS 2020
Marc Fischer, Maximilian Baader, Martin Vechev
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models,
ICML 2020
Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin Vechev
Adversarial Robustness for Code,
ICML 2020
Pavol Bielik, Martin Vechev
Adversarial Training and Provable Defenses: Bridging the Gap,
ICLR 2020Oral presentation
Mislav Balunovic, Martin Vechev
Scalable Inference of Symbolic Adversarial Examples,
arXiv 2020
Dimitar I. Dimitrov, Gagandeep Singh, Martin Vechev
Universal Approximation with Certified Networks,
ICLR 2020
Maximilian Baader, Matthew Mirman, Martin Vechev
Robustness Certification of Generative Models,
arXiv 2020
Mathew Mirman, Timon Gehr, Martin Vechev
Beyond the Single Neuron Convex Barrier for Neural Network Certification,
NeurIPS 2019
Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin Vechev
Certifying Geometric Robustness of Neural Networks,
NeurIPS 2019
Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin Vechev
Online Robustness Training for Deep Reinforcement Learning,
arXiv 2019
Marc Fischer, Matthew Mirman, Steven Stalder, Martin Vechev
DL2: Training and Querying Neural Networks with Logic,
ICML 2019
Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin Vechev
Boosting Robustness Certification of Neural Networks,
ICLR 2019
Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev
An Abstract Domain for Certifying Neural Networks,
POPL 2019
Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev
Fast and Effective Robustness Certification,
NeurIPS 2018
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin Vechev
Differentiable Abstract Interpretation for Provably Robust Neural Networks,
ICML 2018
Matthew Mirman, Timon Gehr, Martin Vechev
AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation,
IEEE S&P 2018
Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, Martin Vechev