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Adam Scibior

Empirical Inference Doctoral Researcher Alumni

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Max-Planck-Ring 4 72076 Tübingen +49 7071 601 551 +49 7071 601 552 [email protected]
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I work on probabilistic programming, which means using techniques from the field of programming languages to make Bayesian modelling easier to use and more accessible. I have also worked on kernel mean embeddings and their applications to probabilistic programming. I am currently based in Cambridge as a part of the Cambridge-Tuebingen PhD programme.

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Empirical Inference Ph.D. Thesis Formally justified and modular Bayesian inference for probabilistic programs Ścibior*, A. University of Cambridge, UK, 2019 (Published) BibTeX
Empirical Inference Conference Paper Denotational Validation of Higher-order Bayesian Inference Ścibior, A., Kammar, O., Vákár, M., Staton, S., Yang, H., Cai, Y., Ostermann, K., Moss, S. K., Heunen, C., Ghahramani, Z. Proceedings of the ACM on Programming Languages, Proceedings of the ACM on Principles of Programming Languages (POPL), 2(Article No. 60):1-29, ACM, 2018 (Published) DOI BibTeX
Empirical Inference Conference Paper Functional Programming for Modular Bayesian Inference Ścibior, A., Kammar, O., Ghahramani, Z. Proceedings of the ACM on Programming Languages, Proceedings of the ACM on Functional Programming (ICFP), 2(Article No. 83):1-29, ACM, 2018 (Published) DOI BibTeX
Empirical Inference Conference Paper Consistent Kernel Mean Estimation for Functions of Random Variables Simon-Gabriel*, C. J., Ścibior*, A., Tolstikhin, I., Schölkopf, B. Advances in Neural Information Processing Systems 29 (NIPS 2016), 1732-1740, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016, *joint first authors (Published) URL BibTeX
Empirical Inference Conference Paper Fabular: Regression Formulas As Probabilistic Programming Borgström, J., Gordon, A. D., Ouyang, L., Russo, C., Ścibior, A., Szymczak, M. Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL), 271-283, POPL ’16, ACM, January 2016 (Published) DOI BibTeX
Empirical Inference Conference Paper Practical Probabilistic Programming with Monads Ścibior, A., Ghahramani, Z., Gordon, A. D. Proceedings of the 2015 ACM SIGPLAN Symposium on Haskell, 165-176, Haskell ’15, ACM, 2015 (Published) DOI BibTeX
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