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.

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.

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.

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.

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.

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.

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.

Revisiting the adversarial robustness-accuracy tradeoff in robot learning
M. Lechner, A. Amini, D. Rus, T.A. Henzinger, ArXiv (n.d.).

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.

Learning verifiable representations
M. Lechner, Learning Verifiable Representations, Institute of Science and Technology Austria, 2022.

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.

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.

Learning stabilizing policies in stochastic control systems
D. Zikelic, M. Lechner, K. Chatterjee, T.A. Henzinger, ArXiv (n.d.).

Learning control policies for stochastic systems with reach-avoid guarantees
D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, ArXiv (n.d.).

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.

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.

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.

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.

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.

Infinite time horizon safety of Bayesian neural networks
M. Lechner, Ð. dikeli, K. Chatterjee, T.A. Henzinger, in:, 35th Conference on Neural Information Processing Systems, 2021.

Interactive analysis of CNN robustness
S. Sietzen, M. Lechner, J. Borowski, R. Hasani, M. Waldner, Computer Graphics Forum 40 (2021) 253–264.

Adversarial training is not ready for robot learning
M. Lechner, R. Hasani, R. Grosu, D. Rus, T.A. Henzinger, in:, 2021 IEEE International Conference on Robotics and Automation, 2021, pp. 4140–4147.

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.

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.

Learning representations for binary-classification without backpropagation
M. Lechner, in:, 8th International Conference on Learning Representations, ICLR, 2020.

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.

An SMT theory of fixed-point arithmetic
M. Baranowski, S. He, M. Lechner, T.S. Nguyen, Z. Rakamari, in:, Automated Reasoning, Springer Nature, 2020, pp. 13–31.

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.

Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme
M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2020, pp. 5446–5452.

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.

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.