Chatterjee K, Henzinger TA, Lechner M, Zikelic D. A learner-verifier framework for neural network controllers and certificates of stochastic systems. In: Tools and Algorithms for the Construction and Analysis of Systems . Vol 13993. Springer Nature; 2023:3-25. doi:10.1007/978-3-031-30823-9_1

Lechner M, Amini A, Rus D, Henzinger TA. Revisiting the adversarial robustness-accuracy tradeoff in robot learning. IEEE Robotics and Automation Letters. 2023;8(3):1595-1602. doi:10.1109/LRA.2023.3240930

Lechner M, Zikelic D, Chatterjee K, Henzinger TA, Rus D. Quantization-aware interval bound propagation for training certifiably robust quantized neural networks. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. Vol 37. Association for the Advancement of Artificial Intelligence; 2023:14964-14973. doi:10.1609/aaai.v37i12.26747

Ansaripour M, Chatterjee K, Henzinger TA, Lechner M, Zikelic D. Learning provably stabilizing neural controllers for discrete-time stochastic systems. In: 21st International Symposium on Automated Technology for Verification and Analysis. Vol 14215. Springer Nature; 2023:357-379. doi:10.1007/978-3-031-45329-8_17

Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies for stochastic systems with reach-avoid guarantees. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. Vol 37. Association for the Advancement of Artificial Intelligence; 2023:11926-11935. doi:10.1609/aaai.v37i10.26407

Zikelic D, Lechner M, Verma A, Chatterjee K, Henzinger TA. Compositional policy learning in stochastic control systems with formal guarantees. In: 37th Conference on Neural Information Processing Systems. ; 2023.

Brunnbauer A, Berducci L, Brandstatter A, et al. Latent imagination facilitates zero-shot transfer in autonomous racing. In: 2022 International Conference on Robotics and Automation. IEEE; 2022:7513-7520. doi:10.1109/ICRA46639.2022.9811650

Lechner M, Amini A, Rus D, Henzinger TA. Revisiting the adversarial robustness-accuracy tradeoff in robot learning. arXiv. doi:10.48550/arXiv.2204.07373

Hasani R, Lechner M, Amini A, et al. Closed-form continuous-time neural networks. Nature Machine Intelligence. 2022;4(11):992-1003. doi:10.1038/s42256-022-00556-7

Lechner M. Learning verifiable representations. 2022. doi:10.15479/at:ista:11362

Gruenbacher SA, Lechner M, Hasani R, et al. GoTube: Scalable statistical verification of continuous-depth models. Proceedings of the AAAI Conference on Artificial Intelligence. 2022;36(6):6755-6764. doi:10.1609/aaai.v36i6.20631

Lechner M, Zikelic D, Chatterjee K, Henzinger TA. Stability verification in stochastic control systems via neural network supermartingales. Proceedings of the AAAI Conference on Artificial Intelligence. 2022;36(7):7326-7336. doi:10.1609/aaai.v36i7.20695

Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. arXiv. doi:10.48550/arXiv.2205.11991

Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies for stochastic systems with reach-avoid guarantees. arXiv. doi:10.48550/ARXIV.2210.05308

Grunbacher S, Hasani R, Lechner M, Cyranka J, Smolka SA, Grosu R. On the verification of neural ODEs with stochastic guarantees. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol 35. AAAI Press; 2021:11525-11535.

Hasani R, Lechner M, Amini A, Rus D, Grosu R. Liquid time-constant networks. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol 35. AAAI Press; 2021:7657-7666.

Babaiee Z, Hasani R, Lechner M, Rus D, Grosu R. On-off center-surround receptive fields for accurate and robust image classification. In: Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:478-489.

Vorbach CJ, Hasani R, Amini A, Lechner M, Rus D. Causal navigation by continuous-time neural networks. In: 35th Conference on Neural Information Processing Systems. ; 2021.

Henzinger TA, Lechner M, Zikelic D. Scalable verification of quantized neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol 35. AAAI Press; 2021:3787-3795.

Lechner M, dikeli Ð, Chatterjee K, Henzinger TA. Infinite time horizon safety of Bayesian neural networks. In: 35th Conference on Neural Information Processing Systems. ; 2021. doi:10.48550/arXiv.2111.03165

Sietzen S, Lechner M, Borowski J, Hasani R, Waldner M. Interactive analysis of CNN robustness. Computer Graphics Forum. 2021;40(7):253-264. doi:10.1111/cgf.14418

Lechner M, Hasani R, Grosu R, Rus D, Henzinger TA. Adversarial training is not ready for robot learning. In: 2021 IEEE International Conference on Robotics and Automation. ICRA. ; 2021:4140-4147. doi:10.1109/ICRA48506.2021.9561036

Hasani R, Lechner M, Amini A, Rus D, Grosu R. A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits. In: Proceedings of the 37th International Conference on Machine Learning. PMLR. ; 2020:4082-4093.

Gruenbacher S, Cyranka J, Lechner M, Islam MA, Smolka SA, Grosu R. Lagrangian reachtubes: The next generation. In: Proceedings of the 59th IEEE Conference on Decision and Control. Vol 2020. IEEE; 2020:1556-1563. doi:10.1109/CDC42340.2020.9304042

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

Giacobbe M, Henzinger TA, Lechner M. How many bits does it take to quantize your neural network? In: International Conference on Tools and Algorithms for the Construction and Analysis of Systems. Vol 12079. Springer Nature; 2020:79-97. doi:10.1007/978-3-030-45237-7_5

Baranowski M, He S, Lechner M, Nguyen TS, Rakamari Z. An SMT theory of fixed-point arithmetic. In: Automated Reasoning. Vol 12166. Springer Nature; 2020:13-31. doi:10.1007/978-3-030-51074-9_2

Lechner M, Hasani R, Amini A, Henzinger TA, Rus D, Grosu R. Neural circuit policies enabling auditable autonomy. Nature Machine Intelligence. 2020;2:642-652. doi:10.1038/s42256-020-00237-3

Lechner M, Hasani R, Rus D, Grosu R. Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. In: Proceedings - IEEE International Conference on Robotics and Automation. IEEE; 2020:5446-5452. doi:10.1109/ICRA40945.2020.9196608

Lechner M, Hasani R, Zimmer M, Henzinger TA, Grosu R. Designing worm-inspired neural networks for interpretable robotic control. In: Proceedings - IEEE International Conference on Robotics and Automation. Vol 2019-May. IEEE; 2019. doi:10.1109/icra.2019.8793840

Hasani R, Amini A, Lechner M, Naser F, Grosu R, Rus D. Response characterization for auditing cell dynamics in long short-term memory networks. In: Proceedings of the International Joint Conference on Neural Networks. IEEE; 2019. doi:10.1109/ijcnn.2019.8851954