{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:32:19Z","timestamp":1770291139842,"version":"3.49.0"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031131844","type":"print"},{"value":"9783031131851","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,8,7]],"date-time":"2022-08-07T00:00:00Z","timestamp":1659830400000},"content-version":"vor","delay-in-days":218,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Morgan and McIver\u2019s <jats:italic>weakest pre-expectation<\/jats:italic> framework is one of the most well-established methods for deductive verification of probabilistic programs. Roughly, the idea is to generalize binary state assertions to real-valued <jats:italic>expectations<\/jats:italic>, which can measure expected values of probabilistic program quantities. While loop-free programs can be analyzed by mechanically transforming expectations, verifying loops usually requires finding an <jats:italic>invariant expectation<\/jats:italic>, a difficult task.<\/jats:p><jats:p>We propose a new view of invariant expectation synthesis as a <jats:italic>regression<\/jats:italic> problem: given an input state, predict the <jats:italic>average<\/jats:italic> value of the post-expectation in the output distribution. Guided by this perspective, we develop the first <jats:italic>data-driven<\/jats:italic> invariant synthesis method for probabilistic programs. Unlike prior work on probabilistic invariant inference, our approach can learn piecewise continuous invariants without relying on template expectations. We also develop a data-driven approach to learn <jats:italic>sub-invariants<\/jats:italic> from data, which can be used to upper- or lower-bound expected values. We implement our approaches and demonstrate their effectiveness on a variety of benchmarks from the probabilistic programming literature.<\/jats:p>","DOI":"10.1007\/978-3-031-13185-1_3","type":"book-chapter","created":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T19:29:09Z","timestamp":1659814149000},"page":"33-54","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Data-Driven Invariant Learning for\u00a0Probabilistic Programs"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2353-350X","authenticated-orcid":false,"given":"Jialu","family":"Bao","sequence":"first","affiliation":[]},{"given":"Nitesh","family":"Trivedi","sequence":"additional","affiliation":[]},{"given":"Drashti","family":"Pathak","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8953-7060","authenticated-orcid":false,"given":"Justin","family":"Hsu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3394-023X","authenticated-orcid":false,"given":"Subhajit","family":"Roy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,7]]},"reference":[{"key":"3_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-81688-9_1","volume-title":"Computer Aided Verification","author":"A Abate","year":"2021","unstructured":"Abate, A., Giacobbe, M., Roy, D.: Learning probabilistic termination proofs. In: Silva, A., Leino, K.R.M. (eds.) CAV 2021. LNCS, vol. 12760, pp. 3\u201326. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-81688-9_1"},{"key":"3_CR2","doi-asserted-by":"publisher","unstructured":"Aguirre, A., Barthe, G., Hsu, J., Kaminski, B.L., Katoen, J.P., Matheja, C.: A pre-expectation calculus for probabilistic sensitivity. In: POPL (2021). https:\/\/doi.org\/10.1145\/3434333","DOI":"10.1145\/3434333"},{"key":"3_CR3","doi-asserted-by":"publisher","unstructured":"Albarghouthi, A., Hsu, J.: Synthesizing coupling proofs of differential privacy. In: POPL (2018). https:\/\/doi.org\/10.1145\/3158146","DOI":"10.1145\/3158146"},{"key":"3_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/3-540-63165-8_199","volume-title":"Automata, Languages and Programming","author":"C Baier","year":"1997","unstructured":"Baier, C., Clarke, E.M., Hartonas-Garmhausen, V., Kwiatkowska, M., Ryan, M.: Symbolic model checking for probabilistic processes. In: Degano, P., Gorrieri, R., Marchetti-Spaccamela, A. (eds.) ICALP 1997. LNCS, vol. 1256, pp. 430\u2013440. Springer, Heidelberg (1997). https:\/\/doi.org\/10.1007\/3-540-63165-8_199"},{"key":"3_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/978-3-319-41528-4_3","volume-title":"Computer Aided Verification","author":"G Barthe","year":"2016","unstructured":"Barthe, G., Espitau, T., Ferrer Fioriti, L.M., Hsu, J.: Synthesizing probabilistic invariants via Doob\u2019s decomposition. In: Chaudhuri, S., Farzan, A. (eds.) CAV 2016. LNCS, vol. 9779, pp. 43\u201361. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-41528-4_3"},{"key":"3_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/978-3-030-31784-3_15","volume-title":"Automated Technology for Verification and Analysis","author":"E Bartocci","year":"2019","unstructured":"Bartocci, E., Kov\u00e1cs, L., Stankovi\u010d, M.: Automatic generation of moment-based invariants for prob-solvable loops. In: Chen, Y.-F., Cheng, C.-H., Esparza, J. (eds.) ATVA 2019. LNCS, vol. 11781, pp. 255\u2013276. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-31784-3_15"},{"key":"3_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1007\/978-3-030-45190-5_28","volume-title":"Tools and Algorithms for the Construction and Analysis of Systems","author":"E Bartocci","year":"2020","unstructured":"Bartocci, E., Kov\u00e1cs, L., Stankovi\u010d, M.: Mora - automatic generation of moment-based invariants. In: Biere, A., Parker, D. (eds.) TACAS 2020. LNCS, vol. 12078, pp. 492\u2013498. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-45190-5_28"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Batz, K., Kaminski, B.L., Katoen, J., Matheja, C.: Relatively complete verification of probabilistic programs: an expressive language for expectation-based reasoning. In: POPL (2021). https:\/\/doi.org\/10.1145\/3434320","DOI":"10.1145\/3434320"},{"key":"3_CR9","doi-asserted-by":"publisher","unstructured":"Carbin, M., Misailovic, S., Rinard, M.C.: Verifying quantitative reliability for programs that execute on unreliable hardware. In: OOPSLA (2013). https:\/\/doi.org\/10.1145\/2509136.2509546","DOI":"10.1145\/2509136.2509546"},{"key":"3_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/978-3-642-39799-8_34","volume-title":"Computer Aided Verification","author":"A Chakarov","year":"2013","unstructured":"Chakarov, A., Sankaranarayanan, S.: Probabilistic program analysis with martingales. In: Sharygina, N., Veith, H. (eds.) CAV 2013. LNCS, vol. 8044, pp. 511\u2013526. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-39799-8_34"},{"key":"3_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/978-3-319-10936-7_6","volume-title":"Static Analysis","author":"A Chakarov","year":"2014","unstructured":"Chakarov, A., Sankaranarayanan, S.: Expectation invariants for probabilistic program loops as fixed points. In: M\u00fcller-Olm, M., Seidl, H. (eds.) SAS 2014. LNCS, vol. 8723, pp. 85\u2013100. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10936-7_6"},{"key":"3_CR12","doi-asserted-by":"publisher","unstructured":"Chatterjee, K., Fu, H., Goharshady, A.K.: Termination analysis of probabilistic programs through Positivstellensatz\u2019s. In: Chaudhuri, S., Farzan, A. (eds.) CAV 2016. LNCS, vol. 9779, pp. 3\u201322. Springer, Cham (2016). ISBN 978-3-319-41528-4. https:\/\/doi.org\/10.1007\/978-3-319-41528-4_1","DOI":"10.1007\/978-3-319-41528-4_1"},{"key":"3_CR13","doi-asserted-by":"publisher","unstructured":"Chatterjee, K., Fu, H., Novotn\u00fd, P., Hasheminezhad, R.: Algorithmic analysis of qualitative and quantitative termination problems for affine probabilistic programs. In: POPL (2016b). https:\/\/doi.org\/10.1145\/2837614.2837639","DOI":"10.1145\/2837614.2837639"},{"key":"3_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1007\/978-3-319-21690-4_44","volume-title":"Computer Aided Verification","author":"Y-F Chen","year":"2015","unstructured":"Chen, Y.-F., Hong, C.-D., Wang, B.-Y., Zhang, L.: Counterexample-guided polynomial loop invariant generation by lagrange interpolation. In: Kroening, D., P\u0103s\u0103reanu, C.S. (eds.) CAV 2015. LNCS, vol. 9206, pp. 658\u2013674. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-21690-4_44"},{"key":"3_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1007\/978-3-319-63390-9_31","volume-title":"Computer Aided Verification","author":"C Dehnert","year":"2017","unstructured":"Dehnert, C., Junges, S., Katoen, J.-P., Volk, M.: A storm is coming: a modern probabilistic model checker. In: Majumdar, R., Kun\u010dak, V. (eds.) CAV 2017. LNCS, vol. 10427, pp. 592\u2013600. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-63390-9_31"},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"Dijkstra, E.W.: Guarded commands, non-determinancy and a calculus for the derivation of programs. In: Language Hierarchies and Interfaces (1975). https:\/\/doi.org\/10.1007\/3-540-07994-7_51","DOI":"10.1007\/3-540-07994-7_51"},{"key":"3_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.scico.2007.01.015","author":"MD Ernst","year":"2007","unstructured":"Ernst, M.D., et al.: The Daikon system for dynamic detection of likely invariants. Sci. Comput. Program. (2007). https:\/\/doi.org\/10.1016\/j.scico.2007.01.015","journal-title":"Sci. Comput. Program."},{"key":"3_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/978-3-319-68167-2_26","volume-title":"Automated Technology for Verification and Analysis","author":"Y Feng","year":"2017","unstructured":"Feng, Y., Zhang, L., Jansen, D.N., Zhan, N., Xia, B.: Finding polynomial loop invariants for probabilistic programs. In: D\u2019Souza, D., Narayan Kumar, K. (eds.) ATVA 2017. LNCS, vol. 10482, pp. 400\u2013416. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68167-2_26"},{"key":"3_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1007\/3-540-45251-6_29","volume-title":"FME 2001: Formal Methods for Increasing Software Productivity","author":"C Flanagan","year":"2001","unstructured":"Flanagan, C., Leino, K.R.M.: Houdini, an annotation assistant for ESC\/Java. In: Oliveira, J.N., Zave, P. (eds.) FME 2001. LNCS, vol. 2021, pp. 500\u2013517. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-45251-6_29"},{"key":"3_CR20","doi-asserted-by":"publisher","unstructured":"Garg, P., Neider, D., Madhusudan, P., Roth, D.: Learning invariants using decision trees and implication counterexamples. In: POPL (2016). https:\/\/doi.org\/10.1145\/2914770.2837664","DOI":"10.1145\/2914770.2837664"},{"key":"3_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-3-642-40196-1_17","volume-title":"Quantitative Evaluation of Systems","author":"F Gretz","year":"2013","unstructured":"Gretz, F., Katoen, J.-P., McIver, A.: Prinsys\u2014On a quest for probabilistic loop invariants. In: Joshi, K., Siegle, M., Stoelinga, M., D\u2019Argenio, P.R. (eds.) QEST 2013. LNCS, vol. 8054, pp. 193\u2013208. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40196-1_17"},{"key":"3_CR22","unstructured":"Kaminski, B.L.: Advanced weakest precondition calculi for probabilistic programs. Ph.D. thesis, RWTH Aachen University, Germany (2019)"},{"key":"3_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1007\/978-3-662-49498-1_15","volume-title":"Programming Languages and Systems","author":"BL Kaminski","year":"2016","unstructured":"Kaminski, B.L., Katoen, J.-P., Matheja, C., Olmedo, F.: Weakest precondition reasoning for expected run\u2013times of probabilistic programs. In: Thiemann, P. (ed.) ESOP 2016. LNCS, vol. 9632, pp. 364\u2013389. Springer, Heidelberg (2016). https:\/\/doi.org\/10.1007\/978-3-662-49498-1_15"},{"key":"3_CR24","doi-asserted-by":"publisher","unstructured":"Kaminski, B.L., Katoen, J.P.: A weakest pre-expectation semantics for mixed-sign expectations. In: LICS (2017). https:\/\/doi.org\/10.5555\/3329995.3330088","DOI":"10.5555\/3329995.3330088"},{"key":"3_CR25","doi-asserted-by":"publisher","unstructured":"Kozen, D.: Semantics of probabilistic programs. J. Comput. Syst. Sci. 22(3) (1981). https:\/\/doi.org\/10.1016\/0022-0000(81)90036-2","DOI":"10.1016\/0022-0000(81)90036-2"},{"key":"3_CR26","doi-asserted-by":"publisher","unstructured":"Kozen, D.: A probabilistic PDL. J. Comput. Syst. Sci. 30(2) (1985). https:\/\/doi.org\/10.1016\/0022-0000(85)90012-1","DOI":"10.1016\/0022-0000(85)90012-1"},{"key":"3_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/978-3-030-17465-1_8","volume-title":"Tools and Algorithms for the Construction and Analysis of Systems","author":"S Kura","year":"2019","unstructured":"Kura, S., Urabe, N., Hasuo, I.: Tail probabilities for randomized program runtimes via martingales for higher moments. In: Vojnar, T., Zhang, L. (eds.) TACAS 2019. LNCS, vol. 11428, pp. 135\u2013153. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-17465-1_8"},{"key":"3_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/978-3-642-22110-1_47","volume-title":"Computer Aided Verification","author":"M Kwiatkowska","year":"2011","unstructured":"Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585\u2013591. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-22110-1_47"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Lahiri, S., Roy, S.: Almost correct invariants: synthesizing inductive invariants by fuzzing proofs. In: ISSTA (2022)","DOI":"10.1145\/3533767.3534381"},{"key":"3_CR30","doi-asserted-by":"publisher","unstructured":"McIver, A., Morgan, C.: Abstraction, Refinement, and Proof for Probabilistic Systems. Springer, New York (2005). https:\/\/doi.org\/10.1007\/b138392","DOI":"10.1007\/b138392"},{"key":"3_CR31","doi-asserted-by":"publisher","unstructured":"McIver, A., Morgan, C., Kaminski, B.L., Katoen, J.: A new proof rule for almost-sure termination. In: POPL (2018). https:\/\/doi.org\/10.1145\/3158121","DOI":"10.1145\/3158121"},{"key":"3_CR32","doi-asserted-by":"publisher","unstructured":"Miltner, A., Padhi, S., Millstein, T., Walker, D.: Data-driven inference of representation invariants. In: PLDI 20 (2020). https:\/\/doi.org\/10.1145\/3385412.3385967","DOI":"10.1145\/3385412.3385967"},{"key":"3_CR33","doi-asserted-by":"publisher","unstructured":"Morgan, C., McIver, A., Seidel, K.: Probabilistic predicate transformers. In: TOPLAS (1996). https:\/\/doi.org\/10.1145\/229542.229547","DOI":"10.1145\/229542.229547"},{"key":"3_CR34","unstructured":"Quinlan, J.R.: Learning with continuous classes. In: AJCAI, vol. 92 (1992)"},{"key":"3_CR35","doi-asserted-by":"publisher","unstructured":"Roy, S., Hsu, J., Albarghouthi, A.: Learning differentially private mechanisms. In: SP (2021). https:\/\/doi.org\/10.1109\/SP40001.2021.00060","DOI":"10.1109\/SP40001.2021.00060"},{"key":"3_CR36","doi-asserted-by":"publisher","unstructured":"Si, X., Dai, H., Raghothaman, M., Naik, M., Song, L.: Learning loop invariants for program verification. In: NeurIPS (2018). https:\/\/doi.org\/10.5555\/3327757.3327873","DOI":"10.5555\/3327757.3327873"},{"key":"3_CR37","doi-asserted-by":"publisher","unstructured":"Smith, C., Hsu, J., Albarghouthi, A.: Trace abstraction modulo probability. In: POPL (2019). https:\/\/doi.org\/10.1145\/3290352","DOI":"10.1145\/3290352"},{"key":"3_CR38","doi-asserted-by":"publisher","unstructured":"Solar-Lezama, A.: Program sketching. Int. J. Softw. Tools Technol. Transf. (2013). https:\/\/doi.org\/10.1007\/s10009-012-0249-7","DOI":"10.1007\/s10009-012-0249-7"},{"key":"3_CR39","doi-asserted-by":"publisher","unstructured":"Wang, D., Hoffmann, J., Reps, T.: Central moment analysis for cost accumulators in probabilistic programs. In: PLDI (2021), https:\/\/doi.org\/10.1145\/3453483.3454062","DOI":"10.1145\/3453483.3454062"},{"key":"3_CR40","doi-asserted-by":"publisher","unstructured":"Wang, D., Hoffmann, J., Reps, T.W.: PMAF: an algebraic framework for static analysis of probabilistic programs. In: PLDI (2018). https:\/\/doi.org\/10.1145\/3192366.3192408","DOI":"10.1145\/3192366.3192408"},{"key":"3_CR41","unstructured":"Yang, Y., Morillo, I.G., Hospedales, T.M.: Deep neural decision trees. CoRR (2018). http:\/\/arxiv.org\/abs\/1806.06988"}],"container-title":["Lecture Notes in Computer Science","Computer Aided Verification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13185-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T17:11:17Z","timestamp":1667495477000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13185-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031131844","9783031131851"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13185-1_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"7 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Aided Verification","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Haifa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cav2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/i-cav.org\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"209","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"9.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}