My Research
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My research goal is to build deep neural networks that we can trust. Specifically, my research includes the topics
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List of Publications
* denotes equal contributions
2023
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Robust flight navigation out of distribution with liquid neural networks
Makram Chahine*, Ramin Hasani*, Patrick Kao*, Aaron Ray, Ryan Shubert, Mathias Lechner, Alexander Amini, Daniela Rus Science Robotics, 2023 |
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Dataset Distillation with Convexified Implicit Gradients
Noel Loo, Ramin Hasani, Mathias Lechner, Daniela Rus Accepted at the International Conference on Machine Learning (ICML), 2023 |
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On the Forward Invariance of Neural ODEs
Wei Xiao, Tsun-Hsuan Wang, Ramin Hasani, Mathias Lechner, Yutong Ban, Chuang Gan, Daniela Rus Accepted at the International Conference on Machine Learning (ICML), 2023 |
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A Learner-Verifier Framework for Neural Network Controllers and Certificates of Stochastic Systems
Krishnendu Chatterjee, Thomas A Henzinger, Mathias Lechner, Đorđe Žikelić International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), 2023 |
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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 IEEE International Conference on Robotics and Automation (ICRA), 2023 |
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Liquid Structural State-Space Models
Ramin Hasani*, Mathias Lechner*, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, Daniela Rus International Conference on Learning Representations (ICLR) , 2023 |
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Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning
Mathias Lechner, Alexander Amini, Daniela Rus, Thomas A. Henzinger IEEE Robotics and Automation Letters (RA-L) , 2023 |
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Quantization-aware Interval Bound Propagation for Training Certifiably Robust
Quantized Neural Networks
Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger, Daniela Rus AAAI Conference on Artificial Intelligence (AAAI) , 2023 |
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Learning Control Policies for Stochastic Systems with Reach-avoid Guarantees
Đorđe Žikelić*, Mathias Lechner*, Thomas A. Henzinger, Krishnendu Chatterjee AAAI Conference on Artificial Intelligence (AAAI) , 2023 |
2022
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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 |
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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 |
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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 |
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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 |
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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 |
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Learning Verifiable Representations
Mathias Lechner PhD thesis, 2022 |
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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 |
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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 |
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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 |
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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
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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 |
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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 |
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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 |
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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 |
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Scalable Verification of
Quantized Neural Networks
Thomas A. Henzinger*, Mathias Lechner*, and Djordje Zikelic* (alphabetical) AAAI Conference on Artificial Intelligence (AAAI) , 2021 |
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Liquid Time-constant
Networks
Ramin Hasani*, Mathias Lechner*, Alexander Amini, Daniela Rus, and Radu Grosu AAAI Conference on Artificial Intelligence (AAAI) , 2021 |
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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
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Neural
circuit policies enabling auditable
autonomy
Mathias Lechner*, Ramin Hasani*, Alexander Amini, Thomas A. Henzinger, Daniela Rus, and Radu Grosu Nature Machine Intelligence, 2020 |
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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 |
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Learning
Representations for binary classification
without backpropagation
Mathias Lechner International Conference on Learning Representations (ICLR), 2020 |
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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 |
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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 |
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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 |
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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
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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 |
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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