{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T21:57:42Z","timestamp":1781128662833,"version":"3.54.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032291042","type":"print"},{"value":"9783032291059","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-29105-9_9","type":"book-chapter","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T21:03:47Z","timestamp":1781125427000},"page":"131-145","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Markov Chain Decoders Overcome the\u00a0Heavy-Tail Limitations of\u00a0Lipschitz Generative Models"],"prefix":"10.1007","author":[{"given":"Abdelhakim","family":"Ziani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andras","family":"Horvath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Ballarini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","unstructured":"Vogel, R.M., Papalexiou, S.M., Lamontagne, J.R., Dolan, F.C.: When heavy tails disrupt statistical inference, The American ... (2025). https:\/\/doi.org\/10.1080\/00031305.2024.2402898. https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/00031305.2024.2402898","DOI":"10.1080\/00031305.2024.2402898"},{"key":"9_CR2","doi-asserted-by":"publisher","unstructured":"Wu, S., Peng, Z., Hu, S.: Heavy tail index estimation for block data. J. Stat. Comput. Simul. (2025). https:\/\/doi.org\/10.1080\/00949655.2025.2566415. https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/00949655.2025.2566415","DOI":"10.1080\/00949655.2025.2566415"},{"key":"9_CR3","unstructured":"Jia, M.: Heavy-tailed phenomena and tail index inference, Doctoral Thesis (2014). https:\/\/tesidottorato.depositolegale.it\/bitstream\/20.500.14242\/93106\/1\/PhD_Thesis_Mofei_Jia.pdf"},{"key":"9_CR4","doi-asserted-by":"publisher","unstructured":"Allouche, M., Girard, S., Gobet, E.: Estimation of extreme quantiles from heavy-tailed distributions with neural networks. Stat. Comput. (2024). https:\/\/doi.org\/10.1007\/s11222-023-10331-2. https:\/\/link.springer.com\/article\/10.1007\/s11222-023-10331-2","DOI":"10.1007\/s11222-023-10331-2"},{"key":"9_CR5","doi-asserted-by":"publisher","unstructured":"Foss, S., Korshunov, D., Zachary, S.: An introduction to heavy-tailed and subexponential distributions. Springer, New York, NY (2011). https:\/\/doi.org\/10.1007\/978-1-4614-7101-1, https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-1-4614-7101-1.pdf","DOI":"10.1007\/978-1-4614-7101-1"},{"key":"9_CR6","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes, CoRR abs\/1312.6114 (2013). https:\/\/api.semanticscholar.org\/CorpusID:216078090"},{"key":"9_CR7","unstructured":"Floto, G., Kremer, S., Nica, M.: The tilted variational autoencoder: improving out-of-distribution detection. In: The Eleventh International Conference on Learning Representations (2023). https:\/\/openreview.net\/forum?id=YlGsTZODyjz"},{"key":"9_CR8","unstructured":"Neuts, M.F.: Probability distributions of phase type. In: Liber Amicorum Professor Emeritus H. Florin, University of Louvain, Belgium, pp. 173\u2013206 (1975)"},{"key":"9_CR9","doi-asserted-by":"publisher","unstructured":"Buchholz, P., Kriege, J., Felko, I.: Phase-Type Distributions, pp. 5\u201328. Springer International Publishing, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-06674-5_2. https:\/\/doi.org\/10.1007\/978-3-319-06674-5_2","DOI":"10.1007\/978-3-319-06674-5_2"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Horv\u00e1th, A., Telek, M.: Phase Type Distributions: Theory and Application, Wiley-ISTE (2024)","DOI":"10.1002\/9781119419808"},{"key":"9_CR11","unstructured":"Huster, T., et al.: Pareto GAN: extending the representational power of GANs to heavy-tailed distributions. In: International Conference on Machine Learning, pp. 4523\u20134532. PMLR (2021)"},{"key":"9_CR12","unstructured":"Jaini, P., Kobyzev, I., Yu, Y., Brubaker, M.: Tails of lipschitz triangular flows. In: International Conference on Machine Learning, pp. 4673\u20134681. PMLR (2020)"},{"issue":"150","key":"9_CR13","first-page":"1","volume":"23","author":"M Allouche","year":"2022","unstructured":"Allouche, M., Girard, S., Gobet, E.: EV-GAN: simulation of extreme events with ReLu neural networks. J. Mach. Learn. Res. 23(150), 1\u201339 (2022)","journal-title":"J. Mach. Learn. Res."},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Bhatia, S., Jain, A., Hooi, B.: ExGan: adversarial generation of extreme samples. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 6750\u20136758 (2021)","DOI":"10.1609\/aaai.v35i8.16834"},{"key":"9_CR15","unstructured":"Lafon, N., Naveau, P., Fablet, R.: A VAE approach to sample multivariate extremes. arXiv preprint arXiv:2306.10987 (2023)"},{"key":"9_CR16","unstructured":"Kim, J., Kwon, J., Cho, M., Lee, H., Won, J.-H.: T3-variational autoencoder: learning heavy-tailed data with student\u2019s t and power divergence. ArXiv abs\/2312.01133 (2023). https:\/\/api.semanticscholar.org\/CorpusID:265608842"},{"key":"9_CR17","unstructured":"Bouayed, A.M., Deslauriers-Gauthier, S., Iaccovelli, A., Naccache, D.: Conditional-T3VAE: equitable latent space allocation for fair generation. ArXiv (2025). https:\/\/api.semanticscholar.org\/CorpusID:281080125"},{"key":"9_CR18","unstructured":"Floto, G., Kremer, S., Nica, M.: The tilted variational autoencoder: improving out-of-distribution detection. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"9_CR19","unstructured":"Horvath, A., Telek, M.: Approximating heavy tailed behavior with phase type distributions. In: Proceedings of 3rd International Conference on Matrix-Analytic Methods in Stochastic models, Notable Publications Inc., pp. 1\u201323 (2000)"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Feldmann, A., Whitt, W.: Fitting mixtures of exponentials to long-tail distributions to analyze network performance models. Perform. Eval. 31, 245\u2013279 (1998)","DOI":"10.1016\/S0166-5316(97)00003-5"},{"key":"9_CR21","doi-asserted-by":"publisher","unstructured":"Ziani, A., Horv\u00e1th, A., Ballarini, P.: Approximating heavy-tailed distributions with a mixture of Bernstein phase-type and hyperexponential models. In: European Workshop on Performance Engineering, pp. 56\u201371. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-032-16345-5_5","DOI":"10.1007\/978-3-032-16345-5_5"},{"key":"9_CR22","unstructured":"Doersch, C.: Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908 (2016). https:\/\/arxiv.org\/abs\/1606.05908"},{"key":"9_CR23","doi-asserted-by":"publisher","unstructured":"Liang, S., Pan, Z., Liu, W., Yin, J., Rijke, M.D.: A survey on variational autoencoders in recommender systems, ACM Comput. Surv. (2024). https:\/\/doi.org\/10.1145\/3663364. https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3663364","DOI":"10.1145\/3663364"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Kingma, D.P., Welling, M., et\u00a0al.: An introduction to variational autoencoders. Found. Trends\u00ae Mach. Learn. 12(4), 307\u2013392 (2019)","DOI":"10.1561\/2200000056"},{"key":"9_CR25","unstructured":"Chen, X., et al.: Variational lossy autoencoder (2017). arXiv:1611.02731. https:\/\/arxiv.org\/abs\/1611.02731"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Stewart, W.J.: Introduction to the Numerical Solution of Markov Chains. Princeton University Press (1994). http:\/\/www.jstor.org\/stable\/j.ctv182jsw5","DOI":"10.1515\/9780691223384"},{"key":"9_CR27","unstructured":"Horv\u00e1th, A., Telek, M.: Approximating heavy tailed behavior with phase-type distributions. In: Proceedings of 3rd International Conference on Matrix-Analytic Methods in Stochastic models, Leuven, Belgium, pp. 191\u2013214 (2000)"},{"key":"9_CR28","doi-asserted-by":"publisher","unstructured":"Cumani, A.: On the canonical representation of homogeneous markov processes modelling failure - time distributions. Microelectronics Reliabil. 22(3), 583\u2013602 (1982). https:\/\/doi.org\/10.1016\/0026-2714(82)90033-6. https:\/\/www.sciencedirect.com\/science\/article\/pii\/0026271482900336","DOI":"10.1016\/0026-2714(82)90033-6"},{"key":"9_CR29","unstructured":"Asmussen, S.: Applied Probability and Queues, 2nd Edition, Vol. 51 of Stochastic Modelling and Applied Probability. Springer, New York (2003)"},{"key":"9_CR30","unstructured":"Grimmett, G., Stirzaker, D.: Probability and random processes. Oxford University Press (2020)"}],"container-title":["Lecture Notes in Computer Science","Computer Performance Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-29105-9_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T21:03:51Z","timestamp":1781125431000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-29105-9_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032291042","9783032291059"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-29105-9_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"11 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPEW","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Workshop on Performance Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grimstad","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Norway","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epew2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epew-workshop.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}