{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T02:57:51Z","timestamp":1791341871385,"version":"4.3.1"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"PNRR ``iNEST'","award":["J43C2200032000"],"award-info":[{"award-number":["J43C2200032000"]}]},{"name":"PRIN ``SEDUCE'","award":["2017TWRCNB"],"award-info":[{"award-number":["2017TWRCNB"]}]},{"name":"`REXASI-PRO' H-EU, HORIZON-CL4-2021-HUMAN-01-01","award":["101070028"],"award-info":[{"award-number":["101070028"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,5,9]]},"DOI":"10.1145\/3575870.3587113","type":"proceedings-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T22:42:27Z","timestamp":1683585747000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6994-6553","authenticated-orcid":false,"given":"Francesca","family":"Cairoli","sequence":"first","affiliation":[{"name":"University of Trieste, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4723-5363","authenticated-orcid":false,"given":"Nicola","family":"Paoletti","sequence":"additional","affiliation":[{"name":"King's College London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8874-4001","authenticated-orcid":false,"given":"Luca","family":"Bortolussi","sequence":"additional","affiliation":[{"name":"University of Trieste, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"ARCH-COMP21 Category Report: Stochastic Models. In 8th International Workshop on Applied Verification of Continuous and Hybrid Systems, ARCH","author":"Abate Alessandro","year":"2021","unstructured":"Alessandro Abate, Henk Blom, Marc Bouissou, Nathalie Cauchi, Hassane Chraibi, Joanna Delicaris, Sofie Haesaert, Arnd Hartmanns, Mahmoud Khaled, Abolfazl Lavaei, 2021. ARCH-COMP21 Category Report: Stochastic Models. In 8th International Workshop on Applied Verification of Continuous and Hybrid Systems, ARCH 2021. EasyChair, 55\u201389."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.3166\/ejc.16.624-641"},{"key":"e_1_3_2_1_3_1","unstructured":"Vineeth Balasubramanian Shen-Shyang Ho and Vladimir Vovk. 2014. Conformal prediction for reliable machine learning: theory adaptations and applications. Newnes."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2015.02.046"},{"key":"e_1_3_2_1_5_1","volume-title":"Lectures on Runtime Verification","author":"Bartocci Ezio","unstructured":"Ezio Bartocci, Jyotirmoy Deshmukh, Alexandre Donz\u00e9, Georgios Fainekos, Oded Maler, Dejan Ni\u010dkovi\u0107, and Sriram Sankaranarayanan. 2018. Specification-based monitoring of cyber-physical systems: a survey on theory, tools and applications. In Lectures on Runtime Verification. Springer, 135\u2013175."},{"key":"e_1_3_2_1_6_1","volume-title":"Stochastic Variational Smoothed Model Checking. arXiv preprint arXiv:2205.05398","author":"Bortolussi Luca","year":"2022","unstructured":"Luca Bortolussi, Francesca Cairoli, Ginevra Carbone, and Paolo Pulcini. 2022. Stochastic Variational Smoothed Model Checking. arXiv preprint arXiv:2205.05398 (2022)."},{"key":"e_1_3_2_1_7_1","volume-title":"Neural Predictive Monitoring. In International Conference on Runtime Verification. Springer, 129\u2013147","author":"Bortolussi Luca","year":"2019","unstructured":"Luca Bortolussi, Francesca Cairoli, Nicola Paoletti, Scott\u00a0A Smolka, and Scott\u00a0D Stoller. 2019. Neural Predictive Monitoring. In International Conference on Runtime Verification. Springer, 129\u2013147."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10009-021-00623-1"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ic.2016.01.004"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Francesca Cairoli Luca Bortolussi and Nicola Paoletti. 2021. Neural Predictive Monitoring under Partial Observability. arxiv:2108.07134\u00a0[cs.LG]","DOI":"10.1007\/978-3-030-88494-9_7"},{"key":"e_1_3_2_1_11_1","volume-title":"Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes. arXiv preprint arXiv:2211.02375","author":"Cairoli Francesca","year":"2022","unstructured":"Francesca Cairoli, Nicola Paoletti, and Luca Bortolussi. 2022. Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes. arXiv preprint arXiv:2211.02375 (2022)."},{"key":"e_1_3_2_1_12_1","volume-title":"Neural Predictive Monitoring for Collective Adaptive Systems. In ISoLA 2022 Symposium","author":"Cairoli Francesca","year":"2022","unstructured":"Francesca Cairoli, Nicola Paoletti, and Luca Bortolussi. 2022. Neural Predictive Monitoring for Collective Adaptive Systems. In ISoLA 2022 Symposium, to appear."},{"key":"e_1_3_2_1_13_1","volume-title":"Predictive Runtime Monitoring of Vehicle Models Using Bayesian Estimation and Reachability Analysis. In Intl. Conference on Intelligent Robots and Systems (IROS).","author":"Chou Yi","year":"2020","unstructured":"Yi Chou, Hansol Yoon, and Sriram Sankaranarayanan. 2020. Predictive Runtime Monitoring of Vehicle Models Using Bayesian Estimation and Reachability Analysis. In Intl. Conference on Intelligent Robots and Systems (IROS)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2006.377184"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15297-9_9"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24743-2_22"},{"key":"e_1_3_2_1_17_1","volume-title":"Weakness Monitors for Fail-Aware Systems. In International Conference on Formal Modeling and Analysis of Timed Systems. Springer, 283\u2013299","author":"Granig Wolfgang","year":"2020","unstructured":"Wolfgang Granig, Stefan Jak\u0161i\u0107, Horst Lewitschnig, Cristinel Mateis, and Dejan Ni\u010dkovi\u0107. 2020. Weakness Monitors for Fail-Aware Systems. In International Conference on Formal Modeling and Analysis of Timed Systems. Springer, 283\u2013299."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3302504.3311806"},{"key":"e_1_3_2_1_19_1","volume-title":"International Conference on Machine Learning. PMLR, 2796\u20132804","author":"Kuleshov Volodymyr","year":"2018","unstructured":"Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon. 2018. Accurate uncertainties for deep learning using calibrated regression. In International Conference on Machine Learning. PMLR, 2796\u20132804."},{"key":"e_1_3_2_1_20_1","volume-title":"Stochastic hybrid models of gene regulatory networks\u2013a pde approach. Mathematical biosciences 305","author":"Kurasov Pavel","year":"2018","unstructured":"Pavel Kurasov, Alexander L\u00fcck, Delio Mugnolo, and Verena Wolf. 2018. Stochastic hybrid models of gene regulatory networks\u2013a pde approach. Mathematical biosciences 305 (2018), 170\u2013177."},{"key":"e_1_3_2_1_21_1","volume-title":"Temporal Robustness of Stochastic Signals. In 25th ACM International Conference on Hybrid Systems: Computation and Control. 1\u201311","author":"Lindemann Lars","year":"2022","unstructured":"Lars Lindemann, Alena Rodionova, and George Pappas. 2022. Temporal Robustness of Stochastic Signals. In 25th ACM International Conference on Hybrid Systems: Computation and Control. 1\u201311."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477032"},{"key":"e_1_3_2_1_23_1","volume-title":"Robotics Research","author":"Majumdar Anirudha","unstructured":"Anirudha Majumdar and Marco Pavone. 2020. How should a robot assess risk? towards an axiomatic theory of risk in robotics. In Robotics Research. Springer, 75\u201384."},{"key":"e_1_3_2_1_24_1","volume-title":"Formal Techniques, Modelling and Analysis of Timed and Fault-Tolerant Systems","author":"Maler Oded","unstructured":"Oded Maler and Dejan Nickovic. 2004. Monitoring temporal properties of continuous signals. In Formal Techniques, Modelling and Analysis of Timed and Fault-Tolerant Systems. Springer, 152\u2013166."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1985.1104071"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Harris Papadopoulos. 2008. Inductive conformal prediction: Theory and application to neural networks. In Tools in artificial intelligence. InTech.","DOI":"10.5772\/6078"},{"key":"e_1_3_2_1_27_1","unstructured":"Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga 2019. PyTorch: An imperative style high-performance deep learning library. In Advances in Neural Information Processing Systems. 8024\u20138035."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01090-4_25"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-55754-6_6"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3302504.3313353"},{"key":"e_1_3_2_1_31_1","volume-title":"Clairvoyant Monitoring for Signal Temporal Logic. In International Conference on Formal Modeling and Analysis of Timed Systems. Springer, 178\u2013195","author":"Qin Xin","year":"2020","unstructured":"Xin Qin and Jyotirmoy\u00a0V Deshmukh. 2020. Clairvoyant Monitoring for Signal Temporal Logic. In International Conference on Formal Modeling and Analysis of Timed Systems. Springer, 178\u2013195."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2014.7039363"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CDC45484.2021.9683477"},{"key":"e_1_3_2_1_34_1","volume-title":"Conformalized quantile regression. arXiv preprint arXiv:1905.03222","author":"Romano Yaniv","year":"2019","unstructured":"Yaniv Romano, Evan Patterson, and Emmanuel\u00a0J Cand\u00e8s. 2019. Conformalized quantile regression. arXiv preprint arXiv:1905.03222 (2019)."},{"key":"e_1_3_2_1_35_1","volume-title":"Classification-based Approximate Reachability with Guarantees Applied to Safe Trajectory Tracking. arXiv preprint arXiv:1803.03237","author":"Royo Vicenc\u00a0Rubies","year":"2018","unstructured":"Vicenc\u00a0Rubies Royo, David Fridovich-Keil, Sylvia Herbert, and Claire\u00a0J Tomlin. 2018. Classification-based Approximate Reachability with Guarantees Applied to Safe Trajectory Tracking. arXiv preprint arXiv:1803.03237 (2018)."},{"key":"e_1_3_2_1_36_1","volume-title":"Algorithmic learning in a random world","author":"Vovk Vladimir","unstructured":"Vladimir Vovk, Alex Gammerman, and Glenn Shafer. 2005. Algorithmic learning in a random world. Springer Science & Business Media."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2020.2981114"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9561193"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.5555\/3220880.3220972"},{"key":"e_1_3_2_1_40_1","volume-title":"Model Predictive Monitoring of Dynamic Systems for Signal Temporal Logic Specifications. arXiv preprint arXiv:2209.12493","author":"Yu Xinyi","year":"2022","unstructured":"Xinyi Yu, Weijie Dong, Xiang Yin, and Shaoyuan Li. 2022. Model Predictive Monitoring of Dynamic Systems for Signal Temporal Logic Specifications. arXiv preprint arXiv:2209.12493 (2022)."}],"event":{"name":"HSCC '23: 26th ACM International Conference on Hybrid Systems: Computation and Control","location":"San Antonio TX USA","acronym":"HSCC '23","sponsor":["SIGBED ACM Special Interest Group on Embedded Systems"]},"container-title":["Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3575870.3587113","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3575870.3587113","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:12Z","timestamp":1750178772000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3575870.3587113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,9]]},"references-count":40,"alternative-id":["10.1145\/3575870.3587113","10.1145\/3575870"],"URL":"https:\/\/doi.org\/10.1145\/3575870.3587113","relation":{},"subject":[],"published":{"date-parts":[[2023,5,9]]},"assertion":[{"value":"2023-05-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}