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Particle Gibbs with Ancestor Sampling for Probabilistic Programs.

, , , and . AISTATS, volume 38 of JMLR Workshop and Conference Proceedings, JMLR.org, (2015)

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Composing Modeling and Inference Operations with Probabilistic Program Combinators., , , and . CoRR, (2018)Learning proposals for probabilistic programs with inference combinators., , , , and . UAI, volume 161 of Proceedings of Machine Learning Research, page 1056-1066. AUAI Press, (2021)Rate-Regularization and Generalization in Variational Autoencoders., , , , , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 3880-3888. PMLR, (2021)Design and Implementation of Probabilistic Programming Language Anglican., , , and . IFL, page 6:1-6:12. ACM, (2016)Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs., , , and . ECML/PKDD (2), volume 9285 of Lecture Notes in Computer Science, page 311-326. Springer, (2015)Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data., , , , and . ICML (2), volume 28 of JMLR Workshop and Conference Proceedings, page 361-369. JMLR.org, (2013)Learning Symmetric Embeddings for Equivariant World Models., , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 17372-17389. PMLR, (2022)An Introduction to Probabilistic Programming, , , and . (2018)cite arxiv:1809.10756Comment: Under review at Foundations and Trends in Machine Learning.Enhancing Few-Shot Image Classification with Unlabelled Examples., , , and . WACV, page 1597-1606. IEEE, (2022)Structured Disentangled Representations., , , , , , , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 2525-2534. PMLR, (2019)