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Celeste: Variational inference for a generative model of astronomical images.

J. Regier, A. Miller, J. McAuliffe, R. Adams, M. Hoffman, D. Lang, D. Schlegel, and Prabhat. ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 2095-2103. JMLR.org, (2015)

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D. Marino

Ph. D. Matthew Millard University of Stuttgart

Matthew Joliffe University of Stuttgart

D. Roth

D. Inkermann

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Stochastic Gradient Descent as Approximate Bayesian Inference.S. Mandt, M. Hoffman, and D. Blei. CoRR, (2017)Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language.M. Hoffman, M. Johnson, and D. Tran. CoRR, (2018)Hedging Strategies for Bayesian OptimizationE. Brochu, M. Hoffman, and N. de Freitas. CoRR, (2010)Making Efficient Use of Demonstrations to Solve Hard Exploration Problems.T. Paine, Ç. Gülçehre, B. Shahriari, M. Denil, M. Hoffman, H. Soyer, R. Tanburn, S. Kapturowski, N. Rabinowitz, D. Williams and 4 other author(s). CoRR, (2019)Speech decoloration based on the product-of-filters model.D. Liang, D. Ellis, M. Hoffman, and G. Mysore. ICASSP, page 2400-2404. IEEE, (2014)Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo.M. Hoffman. ICML, volume 70 of Proceedings of Machine Learning Research, page 1510-1519. PMLR, (2017)An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward.M. Hoffman, N. de Freitas, A. Doucet, and J. Peters. AISTATS, volume 5 of JMLR Proceedings, page 232-239. JMLR.org, (2009)On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning.M. Hoffman, B. Shahriari, and N. de Freitas. AISTATS, volume 33 of JMLR Workshop and Conference Proceedings, page 365-374. JMLR.org, (2014)Variational Autoencoders for Collaborative Filtering.D. Liang, R. Krishnan, M. Hoffman, and T. Jebara. WWW, page 689-698. ACM, (2018)Bayesian Policy Learning with Trans-Dimensional MCMC.M. Hoffman, A. Doucet, N. de Freitas, and A. Jasra. NIPS, page 665-672. Curran Associates, Inc., (2007)