Mathias Lechner

Graduate School
Henzinger_Thomas Group

32 Publications

Mark all

[32]
2024 | Published | Conference Paper | IST-REx-ID: 17898
Overparametrization helps offline-to-online generalization of closed-loop control from pixels
Lechner, Mathias, Overparametrization helps offline-to-online generalization of closed-loop control from pixels. Proceedings of the 2024 IEEE International Conference on Robotics and Automation. 2024
View | DOI
 
[31]
2023 | Published | Journal Article | IST-REx-ID: 12704 | OA
Revisiting the adversarial robustness-accuracy tradeoff in robot learning
M. Lechner, A. Amini, D. Rus, T.A. Henzinger, IEEE Robotics and Automation Letters 8 (2023) 1595–1602.
[Published Version] View | Files available | DOI | WoS | arXiv
 
[30]
2023 | Published | Conference Paper | IST-REx-ID: 15023 | OA
Compositional policy learning in stochastic control systems with formal guarantees
D. Zikelic, M. Lechner, A. Verma, K. Chatterjee, T.A. Henzinger, in:, 37th Conference on Neural Information Processing Systems, 2023.
[Published Version] View | Files available | arXiv
 
[29]
2023 | Published | Conference Paper | IST-REx-ID: 13142 | OA
A learner-verifier framework for neural network controllers and certificates of stochastic systems
K. Chatterjee, T.A. Henzinger, M. Lechner, D. Zikelic, in:, Tools and Algorithms for the Construction and Analysis of Systems , Springer Nature, 2023, pp. 3–25.
[Published Version] View | Files available | DOI
 
[28]
2023 | Published | Conference Paper | IST-REx-ID: 14242 | OA
Quantization-aware interval bound propagation for training certifiably robust quantized neural networks
M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, D. Rus, in:, Proceedings of the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, 2023, pp. 14964–14973.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[27]
2023 | Published | Conference Paper | IST-REx-ID: 14559 | OA
Learning provably stabilizing neural controllers for discrete-time stochastic systems
M. Ansaripour, K. Chatterjee, T.A. Henzinger, M. Lechner, D. Zikelic, in:, 21st International Symposium on Automated Technology for Verification and Analysis, Springer Nature, 2023, pp. 357–379.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[26]
2023 | Published | Conference Paper | IST-REx-ID: 14830
Learning control policies for stochastic systems with reach-avoid guarantees
D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, in:, Proceedings of the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, 2023, pp. 11926–11935.
[Preprint] View | Files available | DOI | arXiv
 
[25]
2022 | Published | Journal Article | IST-REx-ID: 12147 | OA
Closed-form continuous-time neural networks
R. Hasani, M. Lechner, A. Amini, L. Liebenwein, A. Ray, M. Tschaikowski, G. Teschl, D. Rus, Nature Machine Intelligence 4 (2022) 992–1003.
[Published Version] View | Files available | DOI | WoS | arXiv
 
[24]
2022 | Published | Journal Article | IST-REx-ID: 12510 | OA
GoTube: Scalable statistical verification of continuous-depth models
S.A. Gruenbacher, M. Lechner, R. Hasani, D. Rus, T.A. Henzinger, S.A. Smolka, R. Grosu, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022) 6755–6764.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[23]
2022 | Published | Conference Paper | IST-REx-ID: 12010 | OA
Latent imagination facilitates zero-shot transfer in autonomous racing
A. Brunnbauer, L. Berducci, A. Brandstatter, M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, 2022 International Conference on Robotics and Automation, IEEE, 2022, pp. 7513–7520.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[22]
2022 | Draft | Preprint | IST-REx-ID: 11366 | OA
Revisiting the adversarial robustness-accuracy tradeoff in robot learning
M. Lechner, A. Amini, D. Rus, T.A. Henzinger, ArXiv (n.d.).
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[21]
2022 | Published | Journal Article | IST-REx-ID: 12511 | OA
Stability verification in stochastic control systems via neural network supermartingales
M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022) 7326–7336.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[20]
2022 | Draft | Preprint | IST-REx-ID: 14600 | OA
Learning control policies for stochastic systems with reach-avoid guarantees
D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, ArXiv (n.d.).
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[19]
2022 | Draft | Preprint | IST-REx-ID: 14601 | OA
Learning stabilizing policies in stochastic control systems
D. Zikelic, M. Lechner, K. Chatterjee, T.A. Henzinger, ArXiv (n.d.).
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[18]
2022 | Published | Thesis | IST-REx-ID: 11362 | OA
Learning verifiable representations
M. Lechner, Learning Verifiable Representations, Institute of Science and Technology Austria, 2022.
[Published Version] View | Files available | DOI
 
[17]
2021 | Published | Conference Paper | IST-REx-ID: 10669 | OA
On the verification of neural ODEs with stochastic guarantees
S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S.A. Smolka, R. Grosu, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 11525–11535.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[16]
2021 | Published | Conference Paper | IST-REx-ID: 10668 | OA
On-off center-surround receptive fields for accurate and robust image classification
Z. Babaiee, R. Hasani, M. Lechner, D. Rus, R. Grosu, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 478–489.
[Published Version] View | Files available | Download Published Version (ext.)
 
[15]
2021 | Published | Journal Article | IST-REx-ID: 10404 | OA
Interactive analysis of CNN robustness
S. Sietzen, M. Lechner, J. Borowski, R. Hasani, M. Waldner, Computer Graphics Forum 40 (2021) 253–264.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[14]
2021 | Published | Conference Paper | IST-REx-ID: 10671 | OA
Liquid time-constant networks
R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 7657–7666.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[13]
2021 | Published | Conference Paper | IST-REx-ID: 10670 | OA
Causal navigation by continuous-time neural networks
C.J. Vorbach, R. Hasani, A. Amini, M. Lechner, D. Rus, in:, 35th Conference on Neural Information Processing Systems, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[12]
2021 | Published | Conference Paper | IST-REx-ID: 10666 | OA
Adversarial training is not ready for robot learning
Lechner, Mathias, Adversarial training is not ready for robot learning. 2021 IEEE International Conference on Robotics and Automation. 2021
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[11]
2021 | Published | Conference Paper | IST-REx-ID: 10665 | OA
Scalable verification of quantized neural networks
T.A. Henzinger, M. Lechner, D. Zikelic, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 3787–3795.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[10]
2021 | Published | Conference Paper | IST-REx-ID: 10667 | OA
Infinite time horizon safety of Bayesian neural networks
M. Lechner, Ð. Žikelić, K. Chatterjee, T.A. Henzinger, in:, 35th Conference on Neural Information Processing Systems, 2021.
[Published Version] View | Files available | DOI | Download Published Version (ext.) | arXiv
 
[9]
2020 | Published | Conference Paper | IST-REx-ID: 10673 | OA
A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits
R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 4082–4093.
[Published Version] View | Files available | Download Published Version (ext.)
 
[8]
2020 | Published | Conference Paper | IST-REx-ID: 10672 | OA
Learning representations for binary-classification without backpropagation
M. Lechner, in:, 8th International Conference on Learning Representations, ICLR, 2020.
[Published Version] View | Files available | Download Published Version (ext.)
 
[7]
2020 | Published | Conference Paper | IST-REx-ID: 9103 | OA
Lagrangian reachtubes: The next generation
S. Gruenbacher, J. Cyranka, M. Lechner, M.A. Islam, S.A. Smolka, R. Grosu, in:, Proceedings of the 59th IEEE Conference on Decision and Control, IEEE, 2020, pp. 1556–1563.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[6]
2020 | Published | Journal Article | IST-REx-ID: 8679
Neural circuit policies enabling auditable autonomy
M. Lechner, R. Hasani, A. Amini, T.A. Henzinger, D. Rus, R. Grosu, Nature Machine Intelligence 2 (2020) 642–652.
View | Files available | DOI | WoS
 
[5]
2020 | Published | Conference Paper | IST-REx-ID: 8194 | OA
An SMT theory of fixed-point arithmetic
M. Baranowski, S. He, M. Lechner, T.S. Nguyen, Z. Rakamarić, in:, Automated Reasoning, Springer Nature, 2020, pp. 13–31.
[Published Version] View | DOI | Download Published Version (ext.) | WoS
 
[4]
2020 | Published | Conference Paper | IST-REx-ID: 8704 | OA
Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme
Lechner, Mathias, Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. Proceedings - IEEE International Conference on Robotics and Automation. 2020
[Submitted Version] View | Files available | DOI | WoS
 
[3]
2020 | Published | Conference Paper | IST-REx-ID: 7808 | OA
How many bits does it take to quantize your neural network?
M. Giacobbe, T.A. Henzinger, M. Lechner, in:, International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Springer Nature, 2020, pp. 79–97.
[Published Version] View | Files available | DOI
 
[2]
2019 | Published | Conference Paper | IST-REx-ID: 6985 | OA
Response characterization for auditing cell dynamics in long short-term memory networks
R. Hasani, A. Amini, M. Lechner, F. Naser, R. Grosu, D. Rus, in:, Proceedings of the International Joint Conference on Neural Networks, IEEE, 2019.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2019 | Published | Conference Paper | IST-REx-ID: 6888 | OA
Designing worm-inspired neural networks for interpretable robotic control
M. Lechner, R. Hasani, M. Zimmer, T.A. Henzinger, R. Grosu, in:, Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2019.
[Submitted Version] View | Files available | DOI
 
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32 Publications

Mark all

[32]
2024 | Published | Conference Paper | IST-REx-ID: 17898
Overparametrization helps offline-to-online generalization of closed-loop control from pixels
Lechner, Mathias, Overparametrization helps offline-to-online generalization of closed-loop control from pixels. Proceedings of the 2024 IEEE International Conference on Robotics and Automation. 2024
View | DOI
 
[31]
2023 | Published | Journal Article | IST-REx-ID: 12704 | OA
Revisiting the adversarial robustness-accuracy tradeoff in robot learning
M. Lechner, A. Amini, D. Rus, T.A. Henzinger, IEEE Robotics and Automation Letters 8 (2023) 1595–1602.
[Published Version] View | Files available | DOI | WoS | arXiv
 
[30]
2023 | Published | Conference Paper | IST-REx-ID: 15023 | OA
Compositional policy learning in stochastic control systems with formal guarantees
D. Zikelic, M. Lechner, A. Verma, K. Chatterjee, T.A. Henzinger, in:, 37th Conference on Neural Information Processing Systems, 2023.
[Published Version] View | Files available | arXiv
 
[29]
2023 | Published | Conference Paper | IST-REx-ID: 13142 | OA
A learner-verifier framework for neural network controllers and certificates of stochastic systems
K. Chatterjee, T.A. Henzinger, M. Lechner, D. Zikelic, in:, Tools and Algorithms for the Construction and Analysis of Systems , Springer Nature, 2023, pp. 3–25.
[Published Version] View | Files available | DOI
 
[28]
2023 | Published | Conference Paper | IST-REx-ID: 14242 | OA
Quantization-aware interval bound propagation for training certifiably robust quantized neural networks
M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, D. Rus, in:, Proceedings of the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, 2023, pp. 14964–14973.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[27]
2023 | Published | Conference Paper | IST-REx-ID: 14559 | OA
Learning provably stabilizing neural controllers for discrete-time stochastic systems
M. Ansaripour, K. Chatterjee, T.A. Henzinger, M. Lechner, D. Zikelic, in:, 21st International Symposium on Automated Technology for Verification and Analysis, Springer Nature, 2023, pp. 357–379.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[26]
2023 | Published | Conference Paper | IST-REx-ID: 14830
Learning control policies for stochastic systems with reach-avoid guarantees
D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, in:, Proceedings of the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, 2023, pp. 11926–11935.
[Preprint] View | Files available | DOI | arXiv
 
[25]
2022 | Published | Journal Article | IST-REx-ID: 12147 | OA
Closed-form continuous-time neural networks
R. Hasani, M. Lechner, A. Amini, L. Liebenwein, A. Ray, M. Tschaikowski, G. Teschl, D. Rus, Nature Machine Intelligence 4 (2022) 992–1003.
[Published Version] View | Files available | DOI | WoS | arXiv
 
[24]
2022 | Published | Journal Article | IST-REx-ID: 12510 | OA
GoTube: Scalable statistical verification of continuous-depth models
S.A. Gruenbacher, M. Lechner, R. Hasani, D. Rus, T.A. Henzinger, S.A. Smolka, R. Grosu, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022) 6755–6764.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[23]
2022 | Published | Conference Paper | IST-REx-ID: 12010 | OA
Latent imagination facilitates zero-shot transfer in autonomous racing
A. Brunnbauer, L. Berducci, A. Brandstatter, M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, 2022 International Conference on Robotics and Automation, IEEE, 2022, pp. 7513–7520.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[22]
2022 | Draft | Preprint | IST-REx-ID: 11366 | OA
Revisiting the adversarial robustness-accuracy tradeoff in robot learning
M. Lechner, A. Amini, D. Rus, T.A. Henzinger, ArXiv (n.d.).
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[21]
2022 | Published | Journal Article | IST-REx-ID: 12511 | OA
Stability verification in stochastic control systems via neural network supermartingales
M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022) 7326–7336.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[20]
2022 | Draft | Preprint | IST-REx-ID: 14600 | OA
Learning control policies for stochastic systems with reach-avoid guarantees
D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, ArXiv (n.d.).
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[19]
2022 | Draft | Preprint | IST-REx-ID: 14601 | OA
Learning stabilizing policies in stochastic control systems
D. Zikelic, M. Lechner, K. Chatterjee, T.A. Henzinger, ArXiv (n.d.).
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[18]
2022 | Published | Thesis | IST-REx-ID: 11362 | OA
Learning verifiable representations
M. Lechner, Learning Verifiable Representations, Institute of Science and Technology Austria, 2022.
[Published Version] View | Files available | DOI
 
[17]
2021 | Published | Conference Paper | IST-REx-ID: 10669 | OA
On the verification of neural ODEs with stochastic guarantees
S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S.A. Smolka, R. Grosu, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 11525–11535.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[16]
2021 | Published | Conference Paper | IST-REx-ID: 10668 | OA
On-off center-surround receptive fields for accurate and robust image classification
Z. Babaiee, R. Hasani, M. Lechner, D. Rus, R. Grosu, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 478–489.
[Published Version] View | Files available | Download Published Version (ext.)
 
[15]
2021 | Published | Journal Article | IST-REx-ID: 10404 | OA
Interactive analysis of CNN robustness
S. Sietzen, M. Lechner, J. Borowski, R. Hasani, M. Waldner, Computer Graphics Forum 40 (2021) 253–264.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[14]
2021 | Published | Conference Paper | IST-REx-ID: 10671 | OA
Liquid time-constant networks
R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 7657–7666.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[13]
2021 | Published | Conference Paper | IST-REx-ID: 10670 | OA
Causal navigation by continuous-time neural networks
C.J. Vorbach, R. Hasani, A. Amini, M. Lechner, D. Rus, in:, 35th Conference on Neural Information Processing Systems, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[12]
2021 | Published | Conference Paper | IST-REx-ID: 10666 | OA
Adversarial training is not ready for robot learning
Lechner, Mathias, Adversarial training is not ready for robot learning. 2021 IEEE International Conference on Robotics and Automation. 2021
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[11]
2021 | Published | Conference Paper | IST-REx-ID: 10665 | OA
Scalable verification of quantized neural networks
T.A. Henzinger, M. Lechner, D. Zikelic, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 3787–3795.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[10]
2021 | Published | Conference Paper | IST-REx-ID: 10667 | OA
Infinite time horizon safety of Bayesian neural networks
M. Lechner, Ð. Žikelić, K. Chatterjee, T.A. Henzinger, in:, 35th Conference on Neural Information Processing Systems, 2021.
[Published Version] View | Files available | DOI | Download Published Version (ext.) | arXiv
 
[9]
2020 | Published | Conference Paper | IST-REx-ID: 10673 | OA
A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits
R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 4082–4093.
[Published Version] View | Files available | Download Published Version (ext.)
 
[8]
2020 | Published | Conference Paper | IST-REx-ID: 10672 | OA
Learning representations for binary-classification without backpropagation
M. Lechner, in:, 8th International Conference on Learning Representations, ICLR, 2020.
[Published Version] View | Files available | Download Published Version (ext.)
 
[7]
2020 | Published | Conference Paper | IST-REx-ID: 9103 | OA
Lagrangian reachtubes: The next generation
S. Gruenbacher, J. Cyranka, M. Lechner, M.A. Islam, S.A. Smolka, R. Grosu, in:, Proceedings of the 59th IEEE Conference on Decision and Control, IEEE, 2020, pp. 1556–1563.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[6]
2020 | Published | Journal Article | IST-REx-ID: 8679
Neural circuit policies enabling auditable autonomy
M. Lechner, R. Hasani, A. Amini, T.A. Henzinger, D. Rus, R. Grosu, Nature Machine Intelligence 2 (2020) 642–652.
View | Files available | DOI | WoS
 
[5]
2020 | Published | Conference Paper | IST-REx-ID: 8194 | OA
An SMT theory of fixed-point arithmetic
M. Baranowski, S. He, M. Lechner, T.S. Nguyen, Z. Rakamarić, in:, Automated Reasoning, Springer Nature, 2020, pp. 13–31.
[Published Version] View | DOI | Download Published Version (ext.) | WoS
 
[4]
2020 | Published | Conference Paper | IST-REx-ID: 8704 | OA
Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme
Lechner, Mathias, Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. Proceedings - IEEE International Conference on Robotics and Automation. 2020
[Submitted Version] View | Files available | DOI | WoS
 
[3]
2020 | Published | Conference Paper | IST-REx-ID: 7808 | OA
How many bits does it take to quantize your neural network?
M. Giacobbe, T.A. Henzinger, M. Lechner, in:, International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Springer Nature, 2020, pp. 79–97.
[Published Version] View | Files available | DOI
 
[2]
2019 | Published | Conference Paper | IST-REx-ID: 6985 | OA
Response characterization for auditing cell dynamics in long short-term memory networks
R. Hasani, A. Amini, M. Lechner, F. Naser, R. Grosu, D. Rus, in:, Proceedings of the International Joint Conference on Neural Networks, IEEE, 2019.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2019 | Published | Conference Paper | IST-REx-ID: 6888 | OA
Designing worm-inspired neural networks for interpretable robotic control
M. Lechner, R. Hasani, M. Zimmer, T.A. Henzinger, R. Grosu, in:, Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2019.
[Submitted Version] View | Files available | DOI
 
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