{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:04:07Z","timestamp":1743149047238,"version":"3.40.3"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030954666"},{"type":"electronic","value":"9783030954673"}],"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:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-95467-3_30","type":"book-chapter","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T10:07:13Z","timestamp":1643710033000},"page":"413-429","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fast ABC with Joint Generative Modelling and Subset Simulation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1549-7948","authenticated-orcid":false,"given":"Eliane","family":"Maalouf","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2724-2678","authenticated-orcid":false,"given":"David","family":"Ginsbourger","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1613-353X","authenticated-orcid":false,"given":"Niklas","family":"Linde","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"key":"30_CR1","unstructured":"Adler, J., \u00d6ktem, O.: Deep bayesian inversion. arXiv preprint arXiv:1811.05910 (2018)"},{"key":"30_CR2","doi-asserted-by":"publisher","unstructured":"Anantha Padmanabha, G., Zabaras, N.: Solving inverse problems using conditional invertible neural networks. J. Comput. Phys. 433, May 2021. https:\/\/doi.org\/10.1016\/j.jcp.2021.110194","DOI":"10.1016\/j.jcp.2021.110194"},{"key":"30_CR3","unstructured":"Ardizzone, L., Kruse, J., Rother, C., K\u00f6the, U.: Analyzing inverse problems with invertible neural networks. In: International Conference on Learning Representations, ICLR (2018)"},{"issue":"4","key":"30_CR4","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/S0266-8920(01)00019-4","volume":"16","author":"SK Au","year":"2001","unstructured":"Au, S.K., Beck, J.L.: Estimation of small failure probabilities in high dimensions by subset simulation. Probab. Eng. Mech. 16(4), 263\u2013277 (2001). https:\/\/doi.org\/10.1016\/S0266-8920(01)00019-4","journal-title":"Probab. Eng. Mech."},{"issue":"1","key":"30_CR5","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s11222-009-9116-0","volume":"20","author":"MG Blum","year":"2010","unstructured":"Blum, M.G., Fran\u00e7ois, O.: Non-linear regression models for approximate bayesian computation. Stat. Comput. 20(1), 63\u201373 (2010). https:\/\/doi.org\/10.1007\/s11222-009-9116-0","journal-title":"Stat. Comput."},{"key":"30_CR6","doi-asserted-by":"publisher","unstructured":"Calvetti, D., Somersalo, E.: Inverse problems: from regularization to bayesian inference. Wiley Interdisc. Rev. Comput. Stat. 10(3), e1427 (2018). https:\/\/doi.org\/10.1002\/wics.1427","DOI":"10.1002\/wics.1427"},{"key":"30_CR7","unstructured":"Chen, Y., Gutmann, M.U.: Adaptive gaussian copula ABC. In: The 22nd International Conference on Artificial Intelligence and Statistics, pp. 1584\u20131592. PMLR (2019)"},{"issue":"3","key":"30_CR8","doi-asserted-by":"publisher","first-page":"A1339","DOI":"10.1137\/130932831","volume":"36","author":"M Chiachio","year":"2014","unstructured":"Chiachio, M., Beck, J.L., Chiachio, J., Rus, G.: Approximate bayesian computation by subset simulation. SIAM J. Sci. Comput. 36(3), A1339\u2013A1358 (2014). https:\/\/doi.org\/10.1137\/130932831","journal-title":"SIAM J. Sci. Comput."},{"issue":"1","key":"30_CR9","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.jjimei.2020.100004","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53\u201365 (2018). https:\/\/doi.org\/10.1016\/j.jjimei.2020.100004","journal-title":"IEEE Signal Process. Mag."},{"issue":"6","key":"30_CR10","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1111\/gwat.13005","volume":"58","author":"Y Dagasan","year":"2020","unstructured":"Dagasan, Y., Juda, P., Renard, P.: Using generative adversarial networks as a fast forward operator for hydrogeological inverse problems. Groundwater 58(6), 938\u2013950 (2020). https:\/\/doi.org\/10.1111\/gwat.13005","journal-title":"Groundwater"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Ding, X., Wang, Y., Xu, Z., Welch, W.J., Wang, Z.J.: Ccgan: continuous conditional generative adversarial networks for image generation. In: International Conference on Learning Representations, ICLR (2021)","DOI":"10.1007\/978-3-030-91390-8_5"},{"key":"30_CR12","doi-asserted-by":"publisher","unstructured":"Efendiev, Y., Datta-Gupta, A., Ginting, V., Ma, X., Mallick, B.: An efficient two-stage Markov chain Monte Carlo method for dynamic data integration. Water Resour. Res. 41(12) (2005). https:\/\/doi.org\/10.1029\/2004WR003764","DOI":"10.1029\/2004WR003764"},{"key":"30_CR13","unstructured":"Genevay, A., Peyre, G., Cuturi, M.: Learning generative models with sinkhorn divergences. In: International Conference on Artificial Intelligence and Statistics, pp. 1608\u20131617. PMLR (2018)"},{"key":"30_CR14","unstructured":"Greenberg, D., Nonnenmacher, M., Macke, J.: Automatic posterior transformation for likelihood-free inference. In: International Conference on Machine Learning, pp. 2404\u20132414. PMLR (2019)"},{"key":"30_CR15","unstructured":"Gu, S., Ghahramani, Z., Turner, R.: Neural adaptive sequential Monte Carlo. In: Advances in Neural Information Processing Systems, vol. 2, pp. 2629\u20132637 (2015)"},{"issue":"1","key":"30_CR16","first-page":"4256","volume":"17","author":"MU Gutmann","year":"2016","unstructured":"Gutmann, M.U., Corander, J.: Bayesian optimization for likelihood-free inference of simulator-based statistical models. J. Mach. Learn. Res. 17(1), 4256\u20134302 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"J\u00e4rvenp\u00e4\u00e4, M., Gutmann, M., Vehtari, A., Marttinen, P.: Gaussian process modeling in approximate bayesian computation to estimate horizontal gene transfer in bacteria. stat 1050, 21 (2017)","DOI":"10.1214\/18-AOAS1150"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"J\u00e4rvenp\u00e4\u00e4, M., Gutmann, M.U., Vehtari, A., Marttinen, P., et al.: Parallel gaussian process surrogate bayesian inference with noisy likelihood evaluations. Bayesian Analysis (2020)","DOI":"10.1214\/20-BA1200"},{"key":"30_CR20","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.cageo.2015.07.003","volume":"85","author":"L Josset","year":"2015","unstructured":"Josset, L., Demyanov, V., Elsheikh, A.H., Lunati, I.: Accelerating Monte Carlo Markov chains with proxy and error models. Comput. Geosci. 85, 38\u201348 (2015). https:\/\/doi.org\/10.1016\/j.cageo.2015.07.003","journal-title":"Comput. Geosci."},{"key":"30_CR21","unstructured":"Kim, D., Song, K., Kim, Y., Shin, Y., Moon, I.C.: Sequential likelihood-free inference with implicit surrogate proposal. arXiv preprint arXiv:2010.07604 (2020)"},{"key":"30_CR22","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Kingma, D.P., Welling, M., et al.: An introduction to variational autoencoders. Found. Trends Mach. Learn. 12(4), 307\u2013392 (2019)","DOI":"10.1561\/2200000056"},{"key":"30_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992934","author":"I Kobyzev","year":"2020","unstructured":"Kobyzev, I., Prince, S., Brubaker, M.: Normalizing flows: an introduction and review of current methods. IEEE Trans. Pattern Anal. Mach. Intell. (2020). https:\/\/doi.org\/10.1109\/TPAMI.2020.2992934","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR25","unstructured":"Kruse, J., Ardizzone, L., Rother, C., K\u00f6the, U.: Benchmarking invertible architectures on inverse problems. arXiv preprint arXiv:2101.10763 (2021)"},{"key":"30_CR26","doi-asserted-by":"publisher","unstructured":"Laloy, E., H\u00e9rault, R., Jacques, D., Linde, N.: Training-image based geostatistical inversion using a spatial generative adversarial neural network. Water Resour. Res. 54, 381\u2013406 (2018). https:\/\/doi.org\/10.1002\/2017WR022148","DOI":"10.1002\/2017WR022148"},{"key":"30_CR27","doi-asserted-by":"publisher","unstructured":"Laloy, E., H\u00e9rault, R., Lee, J., Jacques, D., Linde, N.: Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network. Adv. Water Resour. 110, 387\u2013405 (2017). https:\/\/doi.org\/10.1016\/j.advwatres.2017.09.029","DOI":"10.1016\/j.advwatres.2017.09.029"},{"key":"30_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.advwatres.2021.103917","volume":"152","author":"E Laloy","year":"2021","unstructured":"Laloy, E., Linde, N., Jacques, D.: Approaching geoscientific inverse problems with vector-to-image domain transfer networks. Adv. Water Resour. 152, 103917 (2021). https:\/\/doi.org\/10.1016\/j.advwatres.2021.103917","journal-title":"Adv. Water Resour."},{"key":"30_CR29","doi-asserted-by":"publisher","unstructured":"Lopez-Alvis, J., Laloy, E., Nguyen, F., Hermans, T.: Deep generative models in inversion: the impact of the generator\u2019s nonlinearity and development of a new approach based on a variational autoencoder. Comput. Geosci. 152 (2021). https:\/\/doi.org\/10.1016\/j.cageo.2021.104762","DOI":"10.1016\/j.cageo.2021.104762"},{"issue":"1","key":"30_CR30","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MSP.2017.2760358","volume":"35","author":"A Lucas","year":"2018","unstructured":"Lucas, A., Iliadis, M., Molina, R., Katsaggelos, A.K.: Using deep neural networks for inverse problems in imaging: beyond analytical methods. IEEE Signal Process. Mag. 35(1), 20\u201336 (2018). https:\/\/doi.org\/10.1109\/MSP.2017.2760358","journal-title":"IEEE Signal Process. Mag."},{"issue":"26","key":"30_CR31","doi-asserted-by":"publisher","first-page":"15324","DOI":"10.1073\/pnas.0306899100","volume":"100","author":"P Marjoram","year":"2003","unstructured":"Marjoram, P., Molitor, J., Plagnol, V., Tavar\u00e9, S.: Markov chain Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. 100(26), 15324\u201315328 (2003). https:\/\/doi.org\/10.1073\/pnas.0306899100","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"6","key":"30_CR32","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/MSP.2017.2739299","volume":"34","author":"MT McCann","year":"2017","unstructured":"McCann, M.T., Jin, K.H., Unser, M.: Convolutional neural networks for inverse problems in imaging: a review. IEEE Signal Process. Mag. 34(6), 85\u201395 (2017). https:\/\/doi.org\/10.1109\/MSP.2017.2739299","journal-title":"IEEE Signal Process. Mag."},{"key":"30_CR33","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"key":"30_CR34","unstructured":"Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: International Conference on Learning Representations, ICLR (2018)"},{"issue":"1","key":"30_CR35","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s11004-019-09832-6","volume":"52","author":"L Mosser","year":"2019","unstructured":"Mosser, L., Dubrule, O., Blunt, M.J.: Stochastic seismic waveform inversion using generative adversarial networks as a geological prior. Math. Geosci. 52(1), 53\u201379 (2019). https:\/\/doi.org\/10.1007\/s11004-019-09832-6","journal-title":"Math. Geosci."},{"key":"30_CR36","doi-asserted-by":"publisher","unstructured":"Mosser, L., Kimman, W., Dramsch, J., Purves, S., De la Fuente Brice\u00f1o, A., Ganssle, G.: Rapid seismic domain transfer: seismic velocity inversion and modeling using deep generative neural networks. In: EAGE Conference and Exhibition (2018). https:\/\/doi.org\/10.3997\/2214-4609.201800734","DOI":"10.3997\/2214-4609.201800734"},{"issue":"1","key":"30_CR37","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1109\/JSAIT.2020.2991563","volume":"1","author":"G Ongie","year":"2020","unstructured":"Ongie, G., Jalal, A., Metzler, C.A., Baraniuk, R.G., Dimakis, A.G., Willett, R.: Deep learning techniques for inverse problems in imaging. IEEE J. Sel. Areas Inf. Theory 1(1), 39\u201356 (2020). https:\/\/doi.org\/10.1109\/JSAIT.2020.2991563","journal-title":"IEEE J. Sel. Areas Inf. Theory"},{"key":"30_CR38","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.probengmech.2015.06.006","volume":"41","author":"I Papaioannou","year":"2015","unstructured":"Papaioannou, I., Betz, W., Zwirglmaier, K., Straub, D.: Mcmc algorithms for subset simulation. Probab. Eng. Mech. 41, 89\u2013103 (2015). https:\/\/doi.org\/10.1016\/j.probengmech.2015.06.006","journal-title":"Probab. Eng. Mech."},{"key":"30_CR39","unstructured":"Papamakarios, G., Sterratt, D., Murray, I.: Sequential neural likelihood: fast likelihood-free inference with autoregressive flows. In: The 22nd International Conference on Artificial Intelligence and Statistics, pp. 837\u2013848. PMLR (2019)"},{"key":"30_CR40","unstructured":"Patrini, G., van den Berg, R., Forr\u00e9, P., Carioni, M., Bhargav, S., Welling, M., Genewein, T., Nielsen, F.: Sinkhorn autoencoders. In: Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, vol. 115, pp. 733\u2013743. PMLR (2020)"},{"key":"30_CR41","unstructured":"Razavi, A., van den Oord, A., Poole, B., Vinyals, O.: Preventing posterior collapse with delta-VAEs. In: International Conference on Learning Representations, ICLR (2019)"},{"key":"30_CR42","unstructured":"Ren, S., Padilla, W., Malof, J.: Benchmarking deep inverse models over time and the neural-adjoint method. In: Advances in Neural Information Processing Systems, vol. 33, pp. 38\u201348 (2020)"},{"key":"30_CR43","unstructured":"Richardson, A.: Generative adversarial networks for model order reduction in seismic full-waveform inversion. arXiv preprint arXiv:1806.00828 (2018)"},{"key":"30_CR44","volume-title":"Monte Carlo statistical methods","author":"C Robert","year":"2013","unstructured":"Robert, C., Casella, G.: Monte Carlo statistical methods. Springer Science & Business Media, New York (2013)"},{"key":"30_CR45","unstructured":"Robert, C.P., Beaumont, M.A., Marin, J.M., Cornuet, J.M.: Adaptivity for abc algorithms: the abc-pmc scheme. arXiv preprint arXiv:0805.2256 (2008)"},{"key":"30_CR46","doi-asserted-by":"publisher","unstructured":"Robert, C.P., Elvira, V., Tawn, N., Wu, C.: Accelerating MCMC algorithms. Wiley Interdisc. Rev. Comput. Stat. 10(5) (2018). https:\/\/doi.org\/10.1002\/wics.1435","DOI":"10.1002\/wics.1435"},{"key":"30_CR47","doi-asserted-by":"publisher","unstructured":"Sisson, S.A., Fan, Y., Beaumont, M.E.: Handbook of Approximate Bayesian Computation. CRC Press (2018). https:\/\/doi.org\/10.1201\/9781315117195","DOI":"10.1201\/9781315117195"},{"key":"30_CR48","unstructured":"Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: Advances in Neural Information Processing Systems, vol. 28, pp. 3483\u20133491 (2015)"},{"key":"30_CR49","doi-asserted-by":"publisher","unstructured":"Tarantola, A.: Inverse problem theory and methods for model parameter estimation, vol. 89. SIAM (2005). https:\/\/doi.org\/10.1137\/1.9780898717921","DOI":"10.1137\/1.9780898717921"},{"key":"30_CR50","unstructured":"Tolstikhin, I., Bousquet, O., Gelly, S., Sch\u00f6lkopf, B.: Wasserstein auto-encoders. In: International Conference on Learning Representations, ICLR (2018)"},{"issue":"179","key":"30_CR51","first-page":"1","volume":"21","author":"F Tonolini","year":"2020","unstructured":"Tonolini, F., Radford, J., Turpin, A., Faccio, D., Murray-Smith, R.: Variational inference for computational imaging inverse problems. J. Mach. Learn. Res. 21(179), 1\u201346 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"30_CR52","unstructured":"Willer, M., Uribe, F.: Subset simulation (2020). https:\/\/www.bgu.tum.de\/era\/software\/software00\/subset-simulation\/. Accessed 08 April 2021"},{"key":"30_CR53","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.compstruc.2011.10.017","volume":"92","author":"KM Zuev","year":"2012","unstructured":"Zuev, K.M., Beck, J.L., Au, S.K., Katafygiotis, L.S.: Bayesian post-processor and other enhancements of subset simulation for estimating failure probabilities in high dimensions. Comput. Struct. 92, 283\u2013296 (2012). https:\/\/doi.org\/10.1016\/j.compstruc.2011.10.017","journal-title":"Comput. Struct."}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95467-3_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T08:32:58Z","timestamp":1680769978000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95467-3_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030954666","9783030954673"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95467-3_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grasmere","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2021.icas.cc\/","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":"215","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":"86","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":"0","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":"40% - 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":"5-6","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":"1-2","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)"}}]}}