Quantitative monitoring of software
T.A. Henzinger, in:, Software Verification, Springer Nature, 2022, pp. 3–6.

Information-flow interfaces
E. Bartocci, T. Ferrere, T.A. Henzinger, D. Nickovic, A.O. Da Costa, in:, Fundamental Approaches to Software Engineering, Springer Nature, 2022, pp. 3–22.

Abstract monitors for quantitative specifications
T.A. Henzinger, N.A. Mazzocchi, N.E. Sarac, in:, 22nd International Conference on Runtime Verification, Springer Nature, 2022, pp. 200–220.

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.

FORQ-based language inclusion formal testing
K. Doveri, P. Ganty, N.A. Mazzocchi, in:, Computer Aided Verification, Springer Nature, 2022, pp. 109–129.

History-deterministic timed automata are not determinizable
S. Bose, T.A. Henzinger, K. Lehtinen, S. Schewe, P. Totzke, in:, 16th International Conference on Reachability Problems, Springer Nature, 2022, pp. 67–76.

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.).

Transitioning from structural to nominal code with efficient gradual typing
F. Mühlböck, R. Tate, Proceedings of the ACM on Programming Languages 5 (2021).

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.

The Civl verifier
B. Kragl, S. Qadeer, in:, P. Ruzica, M.W. Whalen (Eds.), Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design, TU Wien Academic Press, 2021, pp. 143–152.

Formal verification of Zagier's one-sentence proof
G. Dubach, F. Mühlböck, ArXiv (n.d.).

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.