Mathias Lechner

My research goal is to build deep neural networks that we can trust. Specifically, my research includes the topics

  • Formal verification of deep learning systems
  • Interpretable and explainable decision-making in neural networks
  • Robust robot learning

List of Publications

* denotes equal contributions

2022

Closed-form continuous-time neural networks
Ramin Hasani*, Mathias Lechner*, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus
Nature Machine Intelligence, 2022
PyHopper-A Plug-and-Play Hyperparameter Optimization Engine
Mathias Lechner, Ramin Hasani, Sophie Neubauer, Philipp Neubauer, Daniela Rus
Has it Trained Yet? NeurIPS 2022 Workshop, 2022
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap
Mathias Lechner, Ramin Hasani, Alexander Amini, Tsun-Hsuan Wang, Thomas A Henzinger, Daniela Rus
NeurIPS 2022 Machine Learning for Autonomous Driving Workshop (ML4AD), 2022
Mixed-Memory RNNs for Learning Long-term Dependencies in Irregularly-sampled Time Series
Mathias Lechner, Ramin Hasani
Memory in Artificial and Real Intelligence (MemARI) NeurIPS 2022 Workshop, 2022
Infrastructure-based End-to-End Learning and Prevention of Driver Failure
Noam Buckman, Shiva Sreeram, Mathias Lechner, Yutong Ban, Ramin Hasani, Sertac Karaman, Daniela Rus
5th Robot Learning Workshop at NeurIPS: Trustworthy Robotics, 2022
Learning Verifiable Representations
Mathias Lechner
PhD thesis, 2022
Learning Stabilizing Policies in Stochastic Control Systems
Đorđe Žikelić*, Mathias Lechner*, Krishnendu Chatterjee, Thomas A. Henzinger
Workshop on Socially Responsible Machine Learning (SRML) at ICLR 2022, 2022
Stability Verification in Stochastic Control Systems via Neural Network Supermartingales
Mathias Lechner*, Đorđe Žikelić*, Krishnendu Chatterjee, Thomas A. Henzinger
AAAI Conference on Artificial Intelligence (AAAI) , 2022
GoTube: Scalable Stochastic Verification of Continuous-Depth Models
Sophie Gruenbacher, Mathias Lechner, Ramin Hasani, Daniela Rus, Thomas A. Henzinger, Scott A. Smolka, Radu Grosu
AAAI Conference on Artificial Intelligence (AAAI) , 2022
Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars
Axel Brunnbauer*, Luigi Berducci*, Andreas Brandstätter*, Mathias Lechner, Ramin Hasani, Daniela Rus, Radu Grosu
IEEE International Conference on Robotics and Automation (ICRA), 2022

2021

Infinite Time Horizon Safety of Bayesian Neural Networks
Mathias Lechner*, Đorđe Žikelić*, Krishnendu Chatterjee, Thomas A. Henzinger
Conference on Neural Information Processing Systems (NeurIPS), 2021
Causal Navigation by Continuous-time Neural Networks
Charles J Vorbach*, Ramin Hasani*, Alexander Amini, Mathias Lechner, Daniela Rus
Conference on Neural Information Processing Systems (NeurIPS), 2021
On-Off Center-Surround Receptive Fields for Accurate and Robust Image
Zahra Babaiee, Ramin Hasani, Mathias Lechner, Daniela Rus, Radu Grosu
International Conference on Machine Learning (ICML), 2021
Adversarial Training is Not Ready for Robot Learning
Mathias Lechner, Ramin Hasani, Radu Grosu, Daniela Rus, Thomas A. Henzinger
IEEE International Conference on Robotics and Automation (ICRA), 2021
Scalable Verification of Quantized Neural Networks
Thomas A. Henzinger*, Mathias Lechner*, and Djordje Zikelic* (alphabetical)
AAAI Conference on Artificial Intelligence (AAAI) , 2021
Liquid Time-constant Networks
Ramin Hasani*, Mathias Lechner*, Alexander Amini, Daniela Rus, and Radu Grosu
AAAI Conference on Artificial Intelligence (AAAI) , 2021
On the Verification of Neural ODEs with Stochastic Guarantees
Sophie Grünbacher, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott A. Smolka, and Radu Grosu
AAAI Conference on Artificial Intelligence (AAAI) , 2021

2020

Neural circuit policies enabling auditable autonomy
Mathias Lechner*, Ramin Hasani*, Alexander Amini, Thomas A. Henzinger, Daniela Rus, and Radu Grosu
Nature Machine Intelligence, 2020
The Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits
Ramin Hasani*, Mathias Lechner*, Alexander Amini, Daniela Rus, and Radu Grosu
International Conference on Machine Learning (ICML), 2020
Learning Representations for binary classification without backpropagation
Mathias Lechner
International Conference on Learning Representations (ICLR), 2020
An SMT Theory of Fixed-Point Arithmetic
Marek Baranowski, Shaobo He, Mathias Lechner, Thanh Son Nguyen, and Zvonimir Rakamaric
International Joint Conference on Automated Reasoning (IJCAR), 2020
Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-To-End Robot Learning Scheme
Mathias Lechner*, Ramin Hasani*, Daniela Rus, and Radu Grosu
IEEE International Conference on Robotics and Automation (ICRA), 2020
How Many Bits Does it Take to Quantize Your Neural Network? Scheme
Mirco Giacobbe*, Thomas A. Henzinger*, and Mathias Lechner* (alphabetical)
International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), 2020
Lagrangian Reachtubes: The Next Generation
Sophie Gruenbacher, Jacek Cyranka, Mathias Lechner, Md. Ariful Islam, Scott Smolka, and Radu Grosu
TC-DES and TC-HS Outstanding Student Paper Prize
IEEE Conference on Decision and Control (CDC), 2020

2019

Response characterization for auditing cell dynamics in long short-term memory networks
Ramin Hasani*, Alexander Amini*, Mathias Lechner, Felix Naser, Radu Grosu, and Daniela Rus
International Joint Conference on Neural Networks (IJCNN), 2019
Designing worm-inspired neural networks for interpretable robotic control
Mathias Lechner*, Ramin Hasani*, Manuel Zimmer, Thomas A. Henzinger, and Radu Grosu
IEEE International Conference on Robotics and Automation (ICRA), 2019

Awards

Hyperion Research 2022 HPC Innovation Excellence Award for Liquid Machine Learning

Outstanding Reviewer Award 2021 at the IEEE International Conference on Robotics and Automation (ICRA)

First Place In F1TENTH - Autonomous Racing Grand Prix at IFAC 2020

Distinguished Young Alumnus-Award 2018 Faculty of Informatics at TU Wien