K. Chatterjee, T. A. Henzinger, M. Lechner, and D. Zikelic, “A learner-verifier framework for neural network controllers and certificates of stochastic systems,” in Tools and Algorithms for the Construction and Analysis of Systems , Paris, France, 2023, vol. 13993, pp. 3–25.
LibreCat: https://research-explorer.ista.ac.at/record/13142
DOI: 10.1007/978-3-031-30823-9_1

M. Lechner, A. Amini, D. Rus, and T. A. Henzinger, “Revisiting the adversarial robustness-accuracy tradeoff in robot learning,” IEEE Robotics and Automation Letters, vol. 8, no. 3. Institute of Electrical and Electronics Engineers, pp. 1595–1602, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/12704
DOI: 10.1109/LRA.2023.3240930
WoS: 000936534100012
arXiv: 2204.07373

M. Lechner, D. Zikelic, K. Chatterjee, T. A. Henzinger, and D. Rus, “Quantization-aware interval bound propagation for training certifiably robust quantized neural networks,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, United States, 2023, vol. 37, no. 12, pp. 14964–14973.
LibreCat: https://research-explorer.ista.ac.at/record/14242
DOI: 10.1609/aaai.v37i12.26747
arXiv: 2211.16187

M. Ansaripour, K. Chatterjee, T. A. Henzinger, M. Lechner, and D. Zikelic, “Learning provably stabilizing neural controllers for discrete-time stochastic systems,” in 21st International Symposium on Automated Technology for Verification and Analysis, Singapore, Singapore, 2023, vol. 14215, pp. 357–379.
LibreCat: https://research-explorer.ista.ac.at/record/14559
DOI: 10.1007/978-3-031-45329-8_17

D. Zikelic, M. Lechner, T. A. Henzinger, and K. Chatterjee, “Learning control policies for stochastic systems with reach-avoid guarantees,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, United States, 2023, vol. 37, no. 10, pp. 11926–11935.
LibreCat: https://research-explorer.ista.ac.at/record/14830
DOI: 10.1609/aaai.v37i10.26407
arXiv: 2210.05308

D. Zikelic, M. Lechner, A. Verma, K. Chatterjee, and T. A. Henzinger, “Compositional policy learning in stochastic control systems with formal guarantees,” in 37th Conference on Neural Information Processing Systems, New Orleans, LO, United States, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/15023
arXiv: 2312.01456

A. Brunnbauer et al., “Latent imagination facilitates zero-shot transfer in autonomous racing,” in 2022 International Conference on Robotics and Automation, Philadelphia, PA, United States, 2022, pp. 7513–7520.
LibreCat: https://research-explorer.ista.ac.at/record/12010
DOI: 10.1109/ICRA46639.2022.9811650
arXiv: 2103.04909

M. Lechner, A. Amini, D. Rus, and T. A. Henzinger, “Revisiting the adversarial robustness-accuracy tradeoff in robot learning,” arXiv. .
LibreCat: https://research-explorer.ista.ac.at/record/11366
DOI: 10.48550/arXiv.2204.07373
arXiv: 2204.07373

R. Hasani et al., “Closed-form continuous-time neural networks,” Nature Machine Intelligence, vol. 4, no. 11. Springer Nature, pp. 992–1003, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/12147
DOI: 10.1038/s42256-022-00556-7
WoS: 000884215600003
arXiv: 2106.13898

M. Lechner, “Learning verifiable representations,” Institute of Science and Technology Austria, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/11362
DOI: 10.15479/at:ista:11362

S. A. Gruenbacher et al., “GoTube: Scalable statistical verification of continuous-depth models,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 6. Association for the Advancement of Artificial Intelligence, pp. 6755–6764, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/12510
DOI: 10.1609/aaai.v36i6.20631
arXiv: 2107.08467

M. Lechner, D. Zikelic, K. Chatterjee, and T. A. Henzinger, “Stability verification in stochastic control systems via neural network supermartingales,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 7. Association for the Advancement of Artificial Intelligence, pp. 7326–7336, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/12511
DOI: 10.1609/aaai.v36i7.20695
arXiv: 2112.09495

D. Zikelic, M. Lechner, K. Chatterjee, and T. A. Henzinger, “Learning stabilizing policies in stochastic control systems,” arXiv. .
LibreCat: https://research-explorer.ista.ac.at/record/14601
DOI: 10.48550/arXiv.2205.11991
arXiv: 2205.11991

D. Zikelic, M. Lechner, T. A. Henzinger, and K. Chatterjee, “Learning control policies for stochastic systems with reach-avoid guarantees,” arXiv. .
LibreCat: https://research-explorer.ista.ac.at/record/14600
DOI: 10.48550/ARXIV.2210.05308
arXiv: 2210.05308

S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S. A. Smolka, and R. Grosu, “On the verification of neural ODEs with stochastic guarantees,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 13, pp. 11525–11535.
LibreCat: https://research-explorer.ista.ac.at/record/10669
arXiv: 2012.08863

R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “Liquid time-constant networks,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 9, pp. 7657–7666.
LibreCat: https://research-explorer.ista.ac.at/record/10671
arXiv: 2006.04439

Z. Babaiee, R. Hasani, M. Lechner, D. Rus, and R. Grosu, “On-off center-surround receptive fields for accurate and robust image classification,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 478–489.
LibreCat: https://research-explorer.ista.ac.at/record/10668

C. J. Vorbach, R. Hasani, A. Amini, M. Lechner, and D. Rus, “Causal navigation by continuous-time neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10670
arXiv: 2106.08314

T. A. Henzinger, M. Lechner, and D. Zikelic, “Scalable verification of quantized neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 5A, pp. 3787–3795.
LibreCat: https://research-explorer.ista.ac.at/record/10665
arXiv: 2012.08185

M. Lechner, Ð. dikeli, K. Chatterjee, and T. A. Henzinger, “Infinite time horizon safety of Bayesian neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10667
DOI: 10.48550/arXiv.2111.03165
arXiv: 2111.03165

S. Sietzen, M. Lechner, J. Borowski, R. Hasani, and M. Waldner, “Interactive analysis of CNN robustness,” Computer Graphics Forum, vol. 40, no. 7. Wiley, pp. 253–264, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10404
DOI: 10.1111/cgf.14418
WoS: 000722952000024
arXiv: 2110.07667

M. Lechner, R. Hasani, R. Grosu, D. Rus, and T. A. Henzinger, “Adversarial training is not ready for robot learning,” in 2021 IEEE International Conference on Robotics and Automation, Xi’an, China, 2021, pp. 4140–4147.
LibreCat: https://research-explorer.ista.ac.at/record/10666
DOI: 10.1109/ICRA48506.2021.9561036
WoS: 000765738803040
arXiv: 2103.08187

R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits,” in Proceedings of the 37th International Conference on Machine Learning, Virtual, 2020, pp. 4082–4093.
LibreCat: https://research-explorer.ista.ac.at/record/10673

S. Gruenbacher, J. Cyranka, M. Lechner, M. A. Islam, S. A. Smolka, and R. Grosu, “Lagrangian reachtubes: The next generation,” in Proceedings of the 59th IEEE Conference on Decision and Control, Jeju Islang, Korea (South), 2020, vol. 2020, pp. 1556–1563.
LibreCat: https://research-explorer.ista.ac.at/record/9103
DOI: 10.1109/CDC42340.2020.9304042
arXiv: 2012.07458

M. Lechner, “Learning representations for binary-classification without backpropagation,” in 8th International Conference on Learning Representations, Virtualf; Addis Ababa, Ethiopia, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/10672

M. Giacobbe, T. A. Henzinger, and M. Lechner, “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, Dublin, Ireland, 2020, vol. 12079, pp. 79–97.
LibreCat: https://research-explorer.ista.ac.at/record/7808
DOI: 10.1007/978-3-030-45237-7_5

M. Baranowski, S. He, M. Lechner, T. S. Nguyen, and Z. Rakamari, “An SMT theory of fixed-point arithmetic,” in Automated Reasoning, Paris, France, 2020, vol. 12166, pp. 13–31.
LibreCat: https://research-explorer.ista.ac.at/record/8194
DOI: 10.1007/978-3-030-51074-9_2
WoS: 000884318000002

M. Lechner, R. Hasani, A. Amini, T. A. Henzinger, D. Rus, and R. Grosu, “Neural circuit policies enabling auditable autonomy,” Nature Machine Intelligence, vol. 2. Springer Nature, pp. 642–652, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/8679
DOI: 10.1038/s42256-020-00237-3
WoS: 000583337200011

M. Lechner, R. Hasani, D. Rus, and R. Grosu, “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, Paris, France, 2020, pp. 5446–5452.
LibreCat: https://research-explorer.ista.ac.at/record/8704
DOI: 10.1109/ICRA40945.2020.9196608
WoS: 000712319503110

M. Lechner, R. Hasani, M. Zimmer, T. A. Henzinger, and R. Grosu, “Designing worm-inspired neural networks for interpretable robotic control,” in Proceedings - IEEE International Conference on Robotics and Automation, Montreal, QC, Canada, 2019, vol. 2019–May.
LibreCat: https://research-explorer.ista.ac.at/record/6888
DOI: 10.1109/icra.2019.8793840

R. Hasani, A. Amini, M. Lechner, F. Naser, R. Grosu, and D. Rus, “Response characterization for auditing cell dynamics in long short-term memory networks,” in Proceedings of the International Joint Conference on Neural Networks, Budapest, Hungary, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/6985
DOI: 10.1109/ijcnn.2019.8851954
arXiv: 1809.03864