--- _id: '12467' abstract: - lang: eng text: Safety and liveness are elementary concepts of computation, and the foundation of many verification paradigms. The safety-liveness classification of boolean properties characterizes whether a given property can be falsified by observing a finite prefix of an infinite computation trace (always for safety, never for liveness). In quantitative specification and verification, properties assign not truth values, but quantitative values to infinite traces (e.g., a cost, or the distance to a boolean property). We introduce quantitative safety and liveness, and we prove that our definitions induce conservative quantitative generalizations of both (1)~the safety-progress hierarchy of boolean properties and (2)~the safety-liveness decomposition of boolean properties. In particular, we show that every quantitative property can be written as the pointwise minimum of a quantitative safety property and a quantitative liveness property. Consequently, like boolean properties, also quantitative properties can be min-decomposed into safety and liveness parts, or alternatively, max-decomposed into co-safety and co-liveness parts. Moreover, quantitative properties can be approximated naturally. We prove that every quantitative property that has both safe and co-safe approximations can be monitored arbitrarily precisely by a monitor that uses only a finite number of states. acknowledgement: We thank the anonymous reviewers for their helpful comments. This work was supported in part by the ERC-2020-AdG 101020093. alternative_title: - LNCS article_processing_charge: No author: - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Nicolas Adrien full_name: Mazzocchi, Nicolas Adrien id: b26baa86-3308-11ec-87b0-8990f34baa85 last_name: Mazzocchi - first_name: Naci E full_name: Sarac, Naci E id: 8C6B42F8-C8E6-11E9-A03A-F2DCE5697425 last_name: Sarac citation: ama: 'Henzinger TA, Mazzocchi NA, Sarac NE. Quantitative safety and liveness. In: 26th International Conference Foundations of Software Science and Computation Structures. Vol 13992. Springer Nature; 2023:349-370. doi:10.1007/978-3-031-30829-1_17' apa: 'Henzinger, T. A., Mazzocchi, N. A., & Sarac, N. E. (2023). Quantitative safety and liveness. In 26th International Conference Foundations of Software Science and Computation Structures (Vol. 13992, pp. 349–370). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-30829-1_17' chicago: Henzinger, Thomas A, Nicolas Adrien Mazzocchi, and Naci E Sarac. “Quantitative Safety and Liveness.” In 26th International Conference Foundations of Software Science and Computation Structures, 13992:349–70. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-30829-1_17. ieee: T. A. Henzinger, N. A. Mazzocchi, and N. E. Sarac, “Quantitative safety and liveness,” in 26th International Conference Foundations of Software Science and Computation Structures, Paris, France, 2023, vol. 13992, pp. 349–370. ista: 'Henzinger TA, Mazzocchi NA, Sarac NE. 2023. Quantitative safety and liveness. 26th International Conference Foundations of Software Science and Computation Structures. FOSSACS: Foundations of Software Science and Computation Structures, LNCS, vol. 13992, 349–370.' mla: Henzinger, Thomas A., et al. “Quantitative Safety and Liveness.” 26th International Conference Foundations of Software Science and Computation Structures, vol. 13992, Springer Nature, 2023, pp. 349–70, doi:10.1007/978-3-031-30829-1_17. short: T.A. Henzinger, N.A. Mazzocchi, N.E. Sarac, in:, 26th International Conference Foundations of Software Science and Computation Structures, Springer Nature, 2023, pp. 349–370. conference: end_date: 2023-04-27 location: Paris, France name: 'FOSSACS: Foundations of Software Science and Computation Structures' start_date: 2023-04-22 date_created: 2023-01-31T07:23:56Z date_published: 2023-04-21T00:00:00Z date_updated: 2023-07-14T11:20:27Z day: '21' ddc: - '000' department: - _id: GradSch - _id: ToHe doi: 10.1007/978-3-031-30829-1_17 ec_funded: 1 external_id: arxiv: - '2301.11175' file: - access_level: open_access checksum: 981025aed580b6b27c426cb8856cf63e content_type: application/pdf creator: esarac date_created: 2023-01-31T07:22:21Z date_updated: 2023-01-31T07:22:21Z file_id: '12468' file_name: qsl.pdf file_size: 449027 relation: main_file success: 1 - access_level: open_access checksum: f16e2af1e0eb243158ab0f0fe74e7d5a content_type: application/pdf creator: dernst date_created: 2023-06-19T10:28:09Z date_updated: 2023-06-19T10:28:09Z file_id: '13153' file_name: 2023_LNCS_HenzingerT.pdf file_size: 1048171 relation: main_file success: 1 file_date_updated: 2023-06-19T10:28:09Z has_accepted_license: '1' intvolume: ' 13992' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '04' oa: 1 oa_version: Published Version page: 349-370 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: 26th International Conference Foundations of Software Science and Computation Structures publication_identifier: eissn: - 1611-3349 isbn: - '9783031308284' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Quantitative safety and liveness tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 volume: 13992 year: '2023' ... --- _id: '13292' abstract: - lang: eng text: The operator precedence languages (OPLs) represent the largest known subclass of the context-free languages which enjoys all desirable closure and decidability properties. This includes the decidability of language inclusion, which is the ultimate verification problem. Operator precedence grammars, automata, and logics have been investigated and used, for example, to verify programs with arithmetic expressions and exceptions (both of which are deterministic pushdown but lie outside the scope of the visibly pushdown languages). In this paper, we complete the picture and give, for the first time, an algebraic characterization of the class of OPLs in the form of a syntactic congruence that has finitely many equivalence classes exactly for the operator precedence languages. This is a generalization of the celebrated Myhill-Nerode theorem for the regular languages to OPLs. As one of the consequences, we show that universality and language inclusion for nondeterministic operator precedence automata can be solved by an antichain algorithm. Antichain algorithms avoid determinization and complementation through an explicit subset construction, by leveraging a quasi-order on words, which allows the pruning of the search space for counterexample words without sacrificing completeness. Antichain algorithms can be implemented symbolically, and these implementations are today the best-performing algorithms in practice for the inclusion of finite automata. We give a generic construction of the quasi-order needed for antichain algorithms from a finite syntactic congruence. This yields the first antichain algorithm for OPLs, an algorithm that solves the ExpTime-hard language inclusion problem for OPLs in exponential time. acknowledgement: "This work was supported in part by the ERC-2020-AdG 101020093.\r\nWe thank Pierre Ganty for early discussions and the anonymous reviewers for their helpful comments.\r\n" alternative_title: - LIPIcs article_processing_charge: Yes author: - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Pavol full_name: Kebis, Pavol last_name: Kebis - first_name: Nicolas Adrien full_name: Mazzocchi, Nicolas Adrien id: b26baa86-3308-11ec-87b0-8990f34baa85 last_name: Mazzocchi - first_name: Naci E full_name: Sarac, Naci E id: 8C6B42F8-C8E6-11E9-A03A-F2DCE5697425 last_name: Sarac citation: ama: 'Henzinger TA, Kebis P, Mazzocchi NA, Sarac NE. Regular methods for operator precedence languages. In: 50th International Colloquium on Automata, Languages, and Programming. Vol 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2023:129:1--129:20. doi:10.4230/LIPIcs.ICALP.2023.129' apa: 'Henzinger, T. A., Kebis, P., Mazzocchi, N. A., & Sarac, N. E. (2023). Regular methods for operator precedence languages. In 50th International Colloquium on Automata, Languages, and Programming (Vol. 261, p. 129:1--129:20). Paderborn, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.ICALP.2023.129' chicago: Henzinger, Thomas A, Pavol Kebis, Nicolas Adrien Mazzocchi, and Naci E Sarac. “Regular Methods for Operator Precedence Languages.” In 50th International Colloquium on Automata, Languages, and Programming, 261:129:1--129:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. https://doi.org/10.4230/LIPIcs.ICALP.2023.129. ieee: T. A. Henzinger, P. Kebis, N. A. Mazzocchi, and N. E. Sarac, “Regular methods for operator precedence languages,” in 50th International Colloquium on Automata, Languages, and Programming, Paderborn, Germany, 2023, vol. 261, p. 129:1--129:20. ista: 'Henzinger TA, Kebis P, Mazzocchi NA, Sarac NE. 2023. Regular methods for operator precedence languages. 50th International Colloquium on Automata, Languages, and Programming. ICALP: International Colloquium on Automata, Languages, and Programming, LIPIcs, vol. 261, 129:1--129:20.' mla: Henzinger, Thomas A., et al. “Regular Methods for Operator Precedence Languages.” 50th International Colloquium on Automata, Languages, and Programming, vol. 261, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, p. 129:1--129:20, doi:10.4230/LIPIcs.ICALP.2023.129. short: T.A. Henzinger, P. Kebis, N.A. Mazzocchi, N.E. Sarac, in:, 50th International Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, p. 129:1--129:20. conference: end_date: 2023-07-14 location: Paderborn, Germany name: 'ICALP: International Colloquium on Automata, Languages, and Programming' start_date: 2023-07-10 date_created: 2023-07-24T15:11:41Z date_published: 2023-07-05T00:00:00Z date_updated: 2023-07-31T08:38:38Z day: '05' ddc: - '000' department: - _id: GradSch - _id: ToHe doi: 10.4230/LIPIcs.ICALP.2023.129 ec_funded: 1 external_id: arxiv: - '2305.03447' file: - access_level: open_access checksum: 5d4c8932ef3450615a53b9bb15d92eb2 content_type: application/pdf creator: esarac date_created: 2023-07-24T15:11:05Z date_updated: 2023-07-24T15:11:05Z file_id: '13293' file_name: icalp23.pdf file_size: 859379 relation: main_file success: 1 file_date_updated: 2023-07-24T15:11:05Z has_accepted_license: '1' intvolume: ' 261' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 129:1--129:20 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: 50th International Colloquium on Automata, Languages, and Programming publication_identifier: eissn: - 1868-8969 isbn: - '9783959772785' publication_status: published publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik quality_controlled: '1' status: public title: Regular methods for operator precedence languages tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 261 year: '2023' ... --- _id: '12704' abstract: - lang: eng text: Adversarial training (i.e., training on adversarially perturbed input data) is a well-studied method for making neural networks robust to potential adversarial attacks during inference. However, the improved robustness does not come for free but rather is accompanied by a decrease in overall model accuracy and performance. Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off but inflict a net loss when measured in holistic robot performance. This work revisits the robustness-accuracy trade-off in robot learning by systematically analyzing if recent advances in robust training methods and theory in conjunction with adversarial robot learning, are capable of making adversarial training suitable for real-world robot applications. We evaluate three different robot learning tasks ranging from autonomous driving in a high-fidelity environment amenable to sim-to-real deployment to mobile robot navigation and gesture recognition. Our results demonstrate that, while these techniques make incremental improvements on the trade-off on a relative scale, the negative impact on the nominal accuracy caused by adversarial training still outweighs the improved robustness by an order of magnitude. We conclude that although progress is happening, further advances in robust learning methods are necessary before they can benefit robot learning tasks in practice. acknowledgement: "We thank Christoph Lampert for inspiring this work. The\r\nviews and conclusions contained in this document are those of\r\nthe authors and should not be interpreted as representing the\r\nofficial policies, either expressed or implied, of the United States\r\nAir Force or the U.S. Government. The U.S. Government is\r\nauthorized to reproduce and distribute reprints for Government\r\npurposes notwithstanding any copyright notation herein." article_processing_charge: No article_type: original author: - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Alexander full_name: Amini, Alexander last_name: Amini - first_name: Daniela full_name: Rus, Daniela last_name: Rus - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 citation: ama: 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 apa: Lechner, M., Amini, A., Rus, D., & Henzinger, T. A. (2023). Revisiting the adversarial robustness-accuracy tradeoff in robot learning. IEEE Robotics and Automation Letters. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/LRA.2023.3240930 chicago: Lechner, Mathias, Alexander Amini, Daniela Rus, and Thomas A Henzinger. “Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning.” IEEE Robotics and Automation Letters. Institute of Electrical and Electronics Engineers, 2023. https://doi.org/10.1109/LRA.2023.3240930. ieee: 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. ista: Lechner M, Amini A, Rus D, Henzinger TA. 2023. Revisiting the adversarial robustness-accuracy tradeoff in robot learning. IEEE Robotics and Automation Letters. 8(3), 1595–1602. mla: Lechner, Mathias, et al. “Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning.” IEEE Robotics and Automation Letters, vol. 8, no. 3, Institute of Electrical and Electronics Engineers, 2023, pp. 1595–602, doi:10.1109/LRA.2023.3240930. short: M. Lechner, A. Amini, D. Rus, T.A. Henzinger, IEEE Robotics and Automation Letters 8 (2023) 1595–1602. date_created: 2023-03-05T23:01:04Z date_published: 2023-03-01T00:00:00Z date_updated: 2023-08-01T13:36:50Z day: '01' ddc: - '000' department: - _id: ToHe doi: 10.1109/LRA.2023.3240930 external_id: arxiv: - '2204.07373' isi: - '000936534100012' file: - access_level: open_access checksum: 5a75dcd326ea66685de2b1aaec259e85 content_type: application/pdf creator: cchlebak date_created: 2023-03-07T12:22:23Z date_updated: 2023-03-07T12:22:23Z file_id: '12714' file_name: 2023_IEEERobAutLetters_Lechner.pdf file_size: 944052 relation: main_file success: 1 file_date_updated: 2023-03-07T12:22:23Z has_accepted_license: '1' intvolume: ' 8' isi: 1 issue: '3' language: - iso: eng month: '03' oa: 1 oa_version: Published Version page: 1595-1602 publication: IEEE Robotics and Automation Letters publication_identifier: eissn: - 2377-3766 publication_status: published publisher: Institute of Electrical and Electronics Engineers quality_controlled: '1' related_material: record: - id: '11366' relation: earlier_version status: public scopus_import: '1' status: public title: Revisiting the adversarial robustness-accuracy tradeoff in robot learning tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 8 year: '2023' ... --- _id: '12876' abstract: - lang: eng text: "Motivation: The problem of model inference is of fundamental importance to systems biology. Logical models (e.g. Boolean networks; BNs) represent a computationally attractive approach capable of handling large biological networks. The models are typically inferred from experimental data. However, even with a substantial amount of experimental data supported by some prior knowledge, existing inference methods often focus on a small sample of admissible candidate models only.\r\n\r\nResults: We propose Boolean network sketches as a new formal instrument for the inference of Boolean networks. A sketch integrates (typically partial) knowledge about the network’s topology and the update logic (obtained through, e.g. a biological knowledge base or a literature search), as well as further assumptions about the properties of the network’s transitions (e.g. the form of its attractor landscape), and additional restrictions on the model dynamics given by the measured experimental data. Our new BNs inference algorithm starts with an ‘initial’ sketch, which is extended by adding restrictions representing experimental data to a ‘data-informed’ sketch and subsequently computes all BNs consistent with the data-informed sketch. Our algorithm is based on a symbolic representation and coloured model-checking. Our approach is unique in its ability to cover a broad spectrum of knowledge and efficiently produce a compact representation of all inferred BNs. We evaluate the method on a non-trivial collection of real-world and simulated data." acknowledgement: This work was partially supported by GACR [grant No. GA22-10845S]; and Grant Agency of Masaryk University [grant No. MUNI/G/1771/2020]. This work was partially supported by European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie [Grant Agreement No. 101034413 to S.P.]. article_number: btad158 article_processing_charge: No article_type: original author: - first_name: Nikola full_name: Beneš, Nikola last_name: Beneš - first_name: Luboš full_name: Brim, Luboš last_name: Brim - first_name: Ondřej full_name: Huvar, Ondřej last_name: Huvar - first_name: Samuel full_name: Pastva, Samuel id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b last_name: Pastva - first_name: David full_name: Šafránek, David last_name: Šafránek citation: ama: 'Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. Boolean network sketches: A unifying framework for logical model inference. Bioinformatics. 2023;39(4). doi:10.1093/bioinformatics/btad158' apa: 'Beneš, N., Brim, L., Huvar, O., Pastva, S., & Šafránek, D. (2023). Boolean network sketches: A unifying framework for logical model inference. Bioinformatics. Oxford Academic. https://doi.org/10.1093/bioinformatics/btad158' chicago: 'Beneš, Nikola, Luboš Brim, Ondřej Huvar, Samuel Pastva, and David Šafránek. “Boolean Network Sketches: A Unifying Framework for Logical Model Inference.” Bioinformatics. Oxford Academic, 2023. https://doi.org/10.1093/bioinformatics/btad158.' ieee: 'N. Beneš, L. Brim, O. Huvar, S. Pastva, and D. Šafránek, “Boolean network sketches: A unifying framework for logical model inference,” Bioinformatics, vol. 39, no. 4. Oxford Academic, 2023.' ista: 'Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. 2023. Boolean network sketches: A unifying framework for logical model inference. Bioinformatics. 39(4), btad158.' mla: 'Beneš, Nikola, et al. “Boolean Network Sketches: A Unifying Framework for Logical Model Inference.” Bioinformatics, vol. 39, no. 4, btad158, Oxford Academic, 2023, doi:10.1093/bioinformatics/btad158.' short: N. Beneš, L. Brim, O. Huvar, S. Pastva, D. Šafránek, Bioinformatics 39 (2023). date_created: 2023-04-30T22:01:05Z date_published: 2023-04-03T00:00:00Z date_updated: 2023-08-01T14:27:28Z day: '03' ddc: - '000' department: - _id: ToHe doi: 10.1093/bioinformatics/btad158 ec_funded: 1 external_id: isi: - '000976610800001' pmid: - '37004199' file: - access_level: open_access checksum: 2cb90ddf781baefddf47eac4b54e2a03 content_type: application/pdf creator: dernst date_created: 2023-05-02T07:39:04Z date_updated: 2023-05-02T07:39:04Z file_id: '12886' file_name: 2023_Bioinformatics_Benes.pdf file_size: 478740 relation: main_file success: 1 file_date_updated: 2023-05-02T07:39:04Z has_accepted_license: '1' intvolume: ' 39' isi: 1 issue: '4' language: - iso: eng month: '04' oa: 1 oa_version: Published Version pmid: 1 project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: Bioinformatics publication_identifier: eissn: - 1367-4811 publication_status: published publisher: Oxford Academic quality_controlled: '1' related_material: link: - relation: software url: https://doi.org/10.5281/zenodo.7688740 scopus_import: '1' status: public title: 'Boolean network sketches: A unifying framework for logical model inference' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 39 year: '2023' ... --- _id: '14242' abstract: - lang: eng text: We study the problem of training and certifying adversarially robust quantized neural networks (QNNs). Quantization is a technique for making neural networks more efficient by running them using low-bit integer arithmetic and is therefore commonly adopted in industry. Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization, and certification of the quantized representation is necessary to guarantee robustness. In this work, we present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs. Inspired by advances in robust learning of non-quantized networks, our training algorithm computes the gradient of an abstract representation of the actual network. Unlike existing approaches, our method can handle the discrete semantics of QNNs. Based on QA-IBP, we also develop a complete verification procedure for verifying the adversarial robustness of QNNs, which is guaranteed to terminate and produce a correct answer. Compared to existing approaches, the key advantage of our verification procedure is that it runs entirely on GPU or other accelerator devices. We demonstrate experimentally that our approach significantly outperforms existing methods and establish the new state-of-the-art for training and certifying the robustness of QNNs. acknowledgement: "This work was supported in part by the ERC-2020-AdG 101020093, ERC CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 665385. Research was sponsored by the United\r\nStates Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-\r\n1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied,\r\nof the United States Air Force or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright\r\nnotation herein. The research was also funded in part by the AI2050 program at Schmidt Futures (Grant G-22-63172) and Capgemini SE." article_processing_charge: No author: - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic orcid: 0000-0002-4681-1699 - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Daniela full_name: Rus, Daniela last_name: Rus citation: ama: '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' apa: 'Lechner, M., Zikelic, D., Chatterjee, K., Henzinger, T. A., & Rus, D. (2023). Quantization-aware interval bound propagation for training certifiably robust quantized neural networks. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 14964–14973). Washington, DC, United States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v37i12.26747' chicago: Lechner, Mathias, Dorde Zikelic, Krishnendu Chatterjee, Thomas A Henzinger, and Daniela Rus. “Quantization-Aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks.” In Proceedings of the 37th AAAI Conference on Artificial Intelligence, 37:14964–73. Association for the Advancement of Artificial Intelligence, 2023. https://doi.org/10.1609/aaai.v37i12.26747. ieee: 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. ista: 'Lechner M, Zikelic D, Chatterjee K, Henzinger TA, Rus D. 2023. Quantization-aware interval bound propagation for training certifiably robust quantized neural networks. Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence vol. 37, 14964–14973.' mla: Lechner, Mathias, et al. “Quantization-Aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks.” Proceedings of the 37th AAAI Conference on Artificial Intelligence, vol. 37, no. 12, Association for the Advancement of Artificial Intelligence, 2023, pp. 14964–73, doi:10.1609/aaai.v37i12.26747. short: 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. conference: end_date: 2023-02-14 location: Washington, DC, United States name: 'AAAI: Conference on Artificial Intelligence' start_date: 2023-02-07 date_created: 2023-08-27T22:01:17Z date_published: 2023-06-26T00:00:00Z date_updated: 2023-09-05T07:06:14Z day: '26' department: - _id: ToHe - _id: KrCh doi: 10.1609/aaai.v37i12.26747 ec_funded: 1 external_id: arxiv: - '2211.16187' intvolume: ' 37' issue: '12' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2211.16187 month: '06' oa: 1 oa_version: Preprint page: 14964-14973 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: Proceedings of the 37th AAAI Conference on Artificial Intelligence publication_identifier: isbn: - '9781577358800' publication_status: published publisher: Association for the Advancement of Artificial Intelligence quality_controlled: '1' scopus_import: '1' status: public title: Quantization-aware interval bound propagation for training certifiably robust quantized neural networks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2023' ... --- _id: '14243' abstract: - lang: eng text: 'Two-player zero-sum "graph games" are central in logic, verification, and multi-agent systems. The game proceeds by placing a token on a vertex of a graph, and allowing the players to move it to produce an infinite path, which determines the winner or payoff of the game. Traditionally, the players alternate turns in moving the token. In "bidding games", however, the players have budgets and in each turn, an auction (bidding) determines which player moves the token. So far, bidding games have only been studied as full-information games. In this work we initiate the study of partial-information bidding games: we study bidding games in which a player''s initial budget is drawn from a known probability distribution. We show that while for some bidding mechanisms and objectives, it is straightforward to adapt the results from the full-information setting to the partial-information setting, for others, the analysis is significantly more challenging, requires new techniques, and gives rise to interesting results. Specifically, we study games with "mean-payoff" objectives in combination with "poorman" bidding. We construct optimal strategies for a partially-informed player who plays against a fully-informed adversary. We show that, somewhat surprisingly, the "value" under pure strategies does not necessarily exist in such games.' acknowledgement: This research was supported in part by ISF grant no.1679/21, by the ERC CoG 863818 (ForM-SMArt), and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 665385. article_processing_charge: No author: - first_name: Guy full_name: Avni, Guy id: 463C8BC2-F248-11E8-B48F-1D18A9856A87 last_name: Avni orcid: 0000-0001-5588-8287 - first_name: Ismael R full_name: Jecker, Ismael R id: 85D7C63E-7D5D-11E9-9C0F-98C4E5697425 last_name: Jecker - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic orcid: 0000-0002-4681-1699 citation: ama: 'Avni G, Jecker IR, Zikelic D. Bidding graph games with partially-observable budgets. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. Vol 37. ; 2023:5464-5471. doi:10.1609/aaai.v37i5.25679' apa: Avni, G., Jecker, I. R., & Zikelic, D. (2023). Bidding graph games with partially-observable budgets. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 5464–5471). Washington, DC, United States. https://doi.org/10.1609/aaai.v37i5.25679 chicago: Avni, Guy, Ismael R Jecker, and Dorde Zikelic. “Bidding Graph Games with Partially-Observable Budgets.” In Proceedings of the 37th AAAI Conference on Artificial Intelligence, 37:5464–71, 2023. https://doi.org/10.1609/aaai.v37i5.25679. ieee: G. Avni, I. R. Jecker, and D. Zikelic, “Bidding graph games with partially-observable budgets,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, United States, 2023, vol. 37, no. 5, pp. 5464–5471. ista: 'Avni G, Jecker IR, Zikelic D. 2023. Bidding graph games with partially-observable budgets. Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence vol. 37, 5464–5471.' mla: Avni, Guy, et al. “Bidding Graph Games with Partially-Observable Budgets.” Proceedings of the 37th AAAI Conference on Artificial Intelligence, vol. 37, no. 5, 2023, pp. 5464–71, doi:10.1609/aaai.v37i5.25679. short: G. Avni, I.R. Jecker, D. Zikelic, in:, Proceedings of the 37th AAAI Conference on Artificial Intelligence, 2023, pp. 5464–5471. conference: end_date: 2023-02-14 location: Washington, DC, United States name: 'AAAI: Conference on Artificial Intelligence' start_date: 2023-02-07 date_created: 2023-08-27T22:01:18Z date_published: 2023-06-27T00:00:00Z date_updated: 2023-09-05T08:37:00Z day: '27' department: - _id: ToHe - _id: KrCh doi: 10.1609/aaai.v37i5.25679 ec_funded: 1 external_id: arxiv: - '2211.13626' intvolume: ' 37' issue: '5' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1609/aaai.v37i5.25679 month: '06' oa: 1 oa_version: Published Version page: 5464-5471 project: - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: Proceedings of the 37th AAAI Conference on Artificial Intelligence publication_identifier: isbn: - '9781577358800' publication_status: published quality_controlled: '1' scopus_import: '1' status: public title: Bidding graph games with partially-observable budgets type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2023' ... --- _id: '13310' abstract: - lang: eng text: Machine-learned systems are in widespread use for making decisions about humans, and it is important that they are fair, i.e., not biased against individuals based on sensitive attributes. We present runtime verification of algorithmic fairness for systems whose models are unknown, but are assumed to have a Markov chain structure. We introduce a specification language that can model many common algorithmic fairness properties, such as demographic parity, equal opportunity, and social burden. We build monitors that observe a long sequence of events as generated by a given system, and output, after each observation, a quantitative estimate of how fair or biased the system was on that run until that point in time. The estimate is proven to be correct modulo a variable error bound and a given confidence level, where the error bound gets tighter as the observed sequence gets longer. Our monitors are of two types, and use, respectively, frequentist and Bayesian statistical inference techniques. While the frequentist monitors compute estimates that are objectively correct with respect to the ground truth, the Bayesian monitors compute estimates that are correct subject to a given prior belief about the system’s model. Using a prototype implementation, we show how we can monitor if a bank is fair in giving loans to applicants from different social backgrounds, and if a college is fair in admitting students while maintaining a reasonable financial burden on the society. Although they exhibit different theoretical complexities in certain cases, in our experiments, both frequentist and Bayesian monitors took less than a millisecond to update their verdicts after each observation. acknowledgement: 'This work is supported by the European Research Council under Grant No.: ERC-2020-AdG101020093.' alternative_title: - LNCS article_processing_charge: Yes (in subscription journal) author: - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Mahyar full_name: Karimi, Mahyar id: f1dedef5-2f78-11ee-989a-c4c97bccf506 last_name: Karimi orcid: 0009-0005-0820-1696 - first_name: Konstantin full_name: Kueffner, Konstantin id: 8121a2d0-dc85-11ea-9058-af578f3b4515 last_name: Kueffner orcid: 0000-0001-8974-2542 - first_name: Kaushik full_name: Mallik, Kaushik id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598 last_name: Mallik orcid: 0000-0001-9864-7475 citation: ama: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. Monitoring algorithmic fairness. In: Computer Aided Verification. Vol 13965. Springer Nature; 2023:358–382. doi:10.1007/978-3-031-37703-7_17' apa: 'Henzinger, T. A., Karimi, M., Kueffner, K., & Mallik, K. (2023). Monitoring algorithmic fairness. In Computer Aided Verification (Vol. 13965, pp. 358–382). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37703-7_17' chicago: Henzinger, Thomas A, Mahyar Karimi, Konstantin Kueffner, and Kaushik Mallik. “Monitoring Algorithmic Fairness.” In Computer Aided Verification, 13965:358–382. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37703-7_17. ieee: T. A. Henzinger, M. Karimi, K. Kueffner, and K. Mallik, “Monitoring algorithmic fairness,” in Computer Aided Verification, Paris, France, 2023, vol. 13965, pp. 358–382. ista: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. 2023. Monitoring algorithmic fairness. Computer Aided Verification. CAV: Computer Aided Verification, LNCS, vol. 13965, 358–382.' mla: Henzinger, Thomas A., et al. “Monitoring Algorithmic Fairness.” Computer Aided Verification, vol. 13965, Springer Nature, 2023, pp. 358–382, doi:10.1007/978-3-031-37703-7_17. short: T.A. Henzinger, M. Karimi, K. Kueffner, K. Mallik, in:, Computer Aided Verification, Springer Nature, 2023, pp. 358–382. conference: end_date: 2023-07-22 location: Paris, France name: 'CAV: Computer Aided Verification' start_date: 2023-07-17 date_created: 2023-07-25T18:32:40Z date_published: 2023-07-18T00:00:00Z date_updated: 2023-09-05T15:14:00Z day: '18' ddc: - '000' department: - _id: GradSch - _id: ToHe doi: 10.1007/978-3-031-37703-7_17 ec_funded: 1 external_id: arxiv: - '2305.15979' file: - access_level: open_access checksum: ccaf94bf7d658ba012c016e11869b54c content_type: application/pdf creator: dernst date_created: 2023-07-31T08:11:20Z date_updated: 2023-07-31T08:11:20Z file_id: '13327' file_name: 2023_LNCS_CAV_HenzingerT.pdf file_size: 647760 relation: main_file success: 1 file_date_updated: 2023-07-31T08:11:20Z has_accepted_license: '1' intvolume: ' 13965' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 358–382 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: Computer Aided Verification publication_identifier: eisbn: - '9783031377037' eissn: - 1611-3349 isbn: - '9783031377020' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' status: public title: Monitoring algorithmic fairness tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 13965 year: '2023' ... --- _id: '13221' abstract: - lang: eng text: The safety-liveness dichotomy is a fundamental concept in formal languages which plays a key role in verification. Recently, this dichotomy has been lifted to quantitative properties, which are arbitrary functions from infinite words to partially-ordered domains. We look into harnessing the dichotomy for the specific classes of quantitative properties expressed by quantitative automata. These automata contain finitely many states and rational-valued transition weights, and their common value functions Inf, Sup, LimInf, LimSup, LimInfAvg, LimSupAvg, and DSum map infinite words into the totallyordered domain of real numbers. In this automata-theoretic setting, we establish a connection between quantitative safety and topological continuity and provide an alternative characterization of quantitative safety and liveness in terms of their boolean counterparts. For all common value functions, we show how the safety closure of a quantitative automaton can be constructed in PTime, and we provide PSpace-complete checks of whether a given quantitative automaton is safe or live, with the exception of LimInfAvg and LimSupAvg automata, for which the safety check is in ExpSpace. Moreover, for deterministic Sup, LimInf, and LimSup automata, we give PTime decompositions into safe and live automata. These decompositions enable the separation of techniques for safety and liveness verification for quantitative specifications. acknowledgement: We thank Christof Löding for pointing us to some results on PSpace-hardess of universality problems and the anonymous reviewers for their helpful comments. This work was supported in part by the ERC-2020-AdG 101020093 and the Israel Science Foundation grant 2410/22. alternative_title: - LIPIcs article_number: '17' article_processing_charge: No author: - first_name: Udi full_name: Boker, Udi id: 31E297B6-F248-11E8-B48F-1D18A9856A87 last_name: Boker - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Nicolas Adrien full_name: Mazzocchi, Nicolas Adrien id: b26baa86-3308-11ec-87b0-8990f34baa85 last_name: Mazzocchi - first_name: Naci E full_name: Sarac, Naci E id: 8C6B42F8-C8E6-11E9-A03A-F2DCE5697425 last_name: Sarac citation: ama: 'Boker U, Henzinger TA, Mazzocchi NA, Sarac NE. Safety and liveness of quantitative automata. In: 34th International Conference on Concurrency Theory. Vol 279. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2023. doi:10.4230/LIPIcs.CONCUR.2023.17' apa: 'Boker, U., Henzinger, T. A., Mazzocchi, N. A., & Sarac, N. E. (2023). Safety and liveness of quantitative automata. In 34th International Conference on Concurrency Theory (Vol. 279). Antwerp, Belgium: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.CONCUR.2023.17' chicago: Boker, Udi, Thomas A Henzinger, Nicolas Adrien Mazzocchi, and Naci E Sarac. “Safety and Liveness of Quantitative Automata.” In 34th International Conference on Concurrency Theory, Vol. 279. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. https://doi.org/10.4230/LIPIcs.CONCUR.2023.17. ieee: U. Boker, T. A. Henzinger, N. A. Mazzocchi, and N. E. Sarac, “Safety and liveness of quantitative automata,” in 34th International Conference on Concurrency Theory, Antwerp, Belgium, 2023, vol. 279. ista: 'Boker U, Henzinger TA, Mazzocchi NA, Sarac NE. 2023. Safety and liveness of quantitative automata. 34th International Conference on Concurrency Theory. CONCUR: Conference on Concurrency Theory, LIPIcs, vol. 279, 17.' mla: Boker, Udi, et al. “Safety and Liveness of Quantitative Automata.” 34th International Conference on Concurrency Theory, vol. 279, 17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, doi:10.4230/LIPIcs.CONCUR.2023.17. short: U. Boker, T.A. Henzinger, N.A. Mazzocchi, N.E. Sarac, in:, 34th International Conference on Concurrency Theory, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. conference: end_date: 2023-09-23 location: Antwerp, Belgium name: 'CONCUR: Conference on Concurrency Theory' start_date: 2023-09-18 date_created: 2023-07-14T10:00:15Z date_published: 2023-09-01T00:00:00Z date_updated: 2023-10-09T07:14:03Z day: '01' ddc: - '000' department: - _id: GradSch - _id: ToHe doi: 10.4230/LIPIcs.CONCUR.2023.17 ec_funded: 1 external_id: arxiv: - '2307.06016' file: - access_level: open_access checksum: d40e57a04448ea5c77d7e1cfb9590a81 content_type: application/pdf creator: esarac date_created: 2023-07-14T12:03:48Z date_updated: 2023-07-14T12:03:48Z file_id: '13224' file_name: CONCUR23.pdf file_size: 755529 relation: main_file success: 1 file_date_updated: 2023-07-14T12:03:48Z has_accepted_license: '1' intvolume: ' 279' language: - iso: eng month: '09' oa: 1 oa_version: Published Version project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: 34th International Conference on Concurrency Theory publication_identifier: eissn: - 1868-8969 isbn: - '9783959772990' publication_status: published publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik quality_controlled: '1' status: public title: Safety and liveness of quantitative automata tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 279 year: '2023' ... --- _id: '14405' abstract: - lang: eng text: We introduce hypernode automata as a new specification formalism for hyperproperties of concurrent systems. They are finite automata with nodes labeled with hypernode logic formulas and transitions labeled with actions. A hypernode logic formula specifies relations between sequences of variable values in different system executions. Unlike HyperLTL, hypernode logic takes an asynchronous view on execution traces by constraining the values and the order of value changes of each variable without correlating the timing of the changes. Different execution traces are synchronized solely through the transitions of hypernode automata. Hypernode automata naturally combine asynchronicity at the node level with synchronicity at the transition level. We show that the model-checking problem for hypernode automata is decidable over action-labeled Kripke structures, whose actions induce transitions of the specification automata. For this reason, hypernode automaton is a suitable formalism for specifying and verifying asynchronous hyperproperties, such as declassifying observational determinism in multi-threaded programs. acknowledgement: "This work was supported in part by the Austrian Science Fund (FWF) SFB project\r\nSpyCoDe F8502, by the FWF projects ZK-35 and W1255-N23, and by the ERC Advanced Grant\r\nVAMOS 101020093." alternative_title: - LIPIcs article_number: '21' article_processing_charge: Yes author: - first_name: Ezio full_name: Bartocci, Ezio last_name: Bartocci - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Dejan full_name: Nickovic, Dejan id: 41BCEE5C-F248-11E8-B48F-1D18A9856A87 last_name: Nickovic - first_name: Ana full_name: Oliveira da Costa, Ana id: f347ec37-6676-11ee-b395-a888cb7b4fb4 last_name: Oliveira da Costa orcid: 0000-0002-8741-5799 citation: ama: 'Bartocci E, Henzinger TA, Nickovic D, Oliveira da Costa A. Hypernode automata. In: 34th International Conference on Concurrency Theory. Vol 279. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2023. doi:10.4230/LIPIcs.CONCUR.2023.21' apa: 'Bartocci, E., Henzinger, T. A., Nickovic, D., & Oliveira da Costa, A. (2023). Hypernode automata. In 34th International Conference on Concurrency Theory (Vol. 279). Antwerp, Belgium: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.CONCUR.2023.21' chicago: Bartocci, Ezio, Thomas A Henzinger, Dejan Nickovic, and Ana Oliveira da Costa. “Hypernode Automata.” In 34th International Conference on Concurrency Theory, Vol. 279. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. https://doi.org/10.4230/LIPIcs.CONCUR.2023.21. ieee: E. Bartocci, T. A. Henzinger, D. Nickovic, and A. Oliveira da Costa, “Hypernode automata,” in 34th International Conference on Concurrency Theory, Antwerp, Belgium, 2023, vol. 279. ista: 'Bartocci E, Henzinger TA, Nickovic D, Oliveira da Costa A. 2023. Hypernode automata. 34th International Conference on Concurrency Theory. CONCUR: Conference on Concurrency Theory, LIPIcs, vol. 279, 21.' mla: Bartocci, Ezio, et al. “Hypernode Automata.” 34th International Conference on Concurrency Theory, vol. 279, 21, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, doi:10.4230/LIPIcs.CONCUR.2023.21. short: E. Bartocci, T.A. Henzinger, D. Nickovic, A. Oliveira da Costa, in:, 34th International Conference on Concurrency Theory, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. conference: end_date: 2023-09-22 location: Antwerp, Belgium name: 'CONCUR: Conference on Concurrency Theory' start_date: 2023-09-19 date_created: 2023-10-08T22:01:16Z date_published: 2023-09-01T00:00:00Z date_updated: 2023-10-09T07:43:44Z day: '01' ddc: - '000' department: - _id: ToHe doi: 10.4230/LIPIcs.CONCUR.2023.21 ec_funded: 1 external_id: arxiv: - '2305.02836' file: - access_level: open_access checksum: 215765e40454d806174ac0a223e8d6fa content_type: application/pdf creator: dernst date_created: 2023-10-09T07:42:45Z date_updated: 2023-10-09T07:42:45Z file_id: '14413' file_name: 2023_LIPcs_Bartocci.pdf file_size: 795790 relation: main_file success: 1 file_date_updated: 2023-10-09T07:42:45Z has_accepted_license: '1' intvolume: ' 279' language: - iso: eng month: '09' oa: 1 oa_version: Published Version project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: 34th International Conference on Concurrency Theory publication_identifier: isbn: - '9783959772990' issn: - '18688969' publication_status: published publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik quality_controlled: '1' scopus_import: '1' status: public title: Hypernode automata tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 279 year: '2023' ... --- _id: '14454' abstract: - lang: eng text: As AI and machine-learned software are used increasingly for making decisions that affect humans, it is imperative that they remain fair and unbiased in their decisions. To complement design-time bias mitigation measures, runtime verification techniques have been introduced recently to monitor the algorithmic fairness of deployed systems. Previous monitoring techniques assume full observability of the states of the (unknown) monitored system. Moreover, they can monitor only fairness properties that are specified as arithmetic expressions over the probabilities of different events. In this work, we extend fairness monitoring to systems modeled as partially observed Markov chains (POMC), and to specifications containing arithmetic expressions over the expected values of numerical functions on event sequences. The only assumptions we make are that the underlying POMC is aperiodic and starts in the stationary distribution, with a bound on its mixing time being known. These assumptions enable us to estimate a given property for the entire distribution of possible executions of the monitored POMC, by observing only a single execution. Our monitors observe a long run of the system and, after each new observation, output updated PAC-estimates of how fair or biased the system is. The monitors are computationally lightweight and, using a prototype implementation, we demonstrate their effectiveness on several real-world examples. acknowledgement: 'This work is supported by the European Research Council under Grant No.: ERC-2020-AdG 101020093.' alternative_title: - LNCS article_processing_charge: No author: - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Konstantin full_name: Kueffner, Konstantin id: 8121a2d0-dc85-11ea-9058-af578f3b4515 last_name: Kueffner orcid: 0000-0001-8974-2542 - first_name: Kaushik full_name: Mallik, Kaushik id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598 last_name: Mallik orcid: 0000-0001-9864-7475 citation: ama: 'Henzinger TA, Kueffner K, Mallik K. Monitoring algorithmic fairness under partial observations. In: 23rd International Conference on Runtime Verification. Vol 14245. Springer Nature; 2023:291-311. doi:10.1007/978-3-031-44267-4_15' apa: 'Henzinger, T. A., Kueffner, K., & Mallik, K. (2023). Monitoring algorithmic fairness under partial observations. In 23rd International Conference on Runtime Verification (Vol. 14245, pp. 291–311). Thessaloniki, Greece: Springer Nature. https://doi.org/10.1007/978-3-031-44267-4_15' chicago: Henzinger, Thomas A, Konstantin Kueffner, and Kaushik Mallik. “Monitoring Algorithmic Fairness under Partial Observations.” In 23rd International Conference on Runtime Verification, 14245:291–311. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-44267-4_15. ieee: T. A. Henzinger, K. Kueffner, and K. Mallik, “Monitoring algorithmic fairness under partial observations,” in 23rd International Conference on Runtime Verification, Thessaloniki, Greece, 2023, vol. 14245, pp. 291–311. ista: 'Henzinger TA, Kueffner K, Mallik K. 2023. Monitoring algorithmic fairness under partial observations. 23rd International Conference on Runtime Verification. RV: Conference on Runtime Verification, LNCS, vol. 14245, 291–311.' mla: Henzinger, Thomas A., et al. “Monitoring Algorithmic Fairness under Partial Observations.” 23rd International Conference on Runtime Verification, vol. 14245, Springer Nature, 2023, pp. 291–311, doi:10.1007/978-3-031-44267-4_15. short: T.A. Henzinger, K. Kueffner, K. Mallik, in:, 23rd International Conference on Runtime Verification, Springer Nature, 2023, pp. 291–311. conference: end_date: 2023-10-06 location: Thessaloniki, Greece name: 'RV: Conference on Runtime Verification' start_date: 2023-10-03 date_created: 2023-10-29T23:01:15Z date_published: 2023-10-01T00:00:00Z date_updated: 2023-10-31T11:48:20Z day: '01' department: - _id: ToHe doi: 10.1007/978-3-031-44267-4_15 ec_funded: 1 external_id: arxiv: - '2308.00341' intvolume: ' 14245' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2308.00341 month: '10' oa: 1 oa_version: Preprint page: 291-311 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: 23rd International Conference on Runtime Verification publication_identifier: eissn: - 1611-3349 isbn: - '9783031442667' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Monitoring algorithmic fairness under partial observations type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 14245 year: '2023' ...