2026-07-22T18:45:13Z https://drops.dagstuhl.de/oai

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Learning through failure Sato, Taisuke Kameya, Yoshitaka Program transformation failure generative modeling PRISM, a symbolic-statistical modeling language we have been developing since '97, recently incorporated a program transformation technique to handle failure in generative modeling. I'll show this feature opens a way to new breeds of symbolic models, including EM learning from negative observations, constrained HMMs and finite PCFGs. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Taisuke Sato and Yoshitaka Kameya 2006 Is Part Of Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.05051.9 urn:nbn:de:0030-drops-4185 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.9 eng https://creativecommons.org/licenses/by/4.0/legalcode