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PROGRAMMING LANGUAGES
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34 resources-
Abramsky, S., Haghverdi, E., & Scott, P. (2002). Geometry of Interaction and Linear Combinatory Algebras. Mathematical. Structures in Comp. Sci., 12(5), 625–665. https://doi.org/10/fcsmhm
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Baudart, G., Mandel, L., Atkinson, E., Sherman, B., Pouzet, M., & Carbin, M. (2019). Reactive Probabilistic Programming. ArXiv:1908.07563 [Cs]. Retrieved from http://arxiv.org/abs/1908.07563
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Borgström, J., Lago, U. D., Gordon, A. D., & Szymczak, M. (2017). A Lambda-Calculus Foundation for Universal Probabilistic Programming. ArXiv:1512.08990 [Cs]. Retrieved from http://arxiv.org/abs/1512.08990
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Boutillier, P., Maasha, M., Li, X., Medina-Abarca, H. F., Krivine, J., Feret, J., … Fontana, W. (2018). The Kappa platform for rule-based modeling. Bioinformatics, 34(13), i583–i592. https://doi.org/10/gdrhw6
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Desharnais, J., Edalat, A., & Panangaden, P. (2002). Bisimulation for Labelled Markov Processes. Information and Computation, 179(2), 163–193. https://doi.org/10/fmp9vd
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Ehrhard, T. (2016). An introduction to Differential Linear Logic: proof-nets, models and antiderivatives. ArXiv:1606.01642 [Cs]. Retrieved from http://arxiv.org/abs/1606.01642
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Ehrhard, T. (2019). Differentials and distances in probabilistic coherence spaces. ArXiv:1902.04836 [Cs]. Retrieved from http://arxiv.org/abs/1902.04836
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Ehrhard, T., & Danos, V. (2011). Probabilistic coherence spaces as a model of higher-order probabilistic computation. Information and Computation, 209(6), 966–991. https://doi.org/10/ctfch6
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Ehrhard, T., Pagani, M., & Tasson, C. (2017). Measurable Cones and Stable, Measurable Functions. Proceedings of the ACM on Programming Languages, 2(POPL), 1–28. https://doi.org/10/ggdjf8
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Ehrhard, T., & Regnier, L. (2003). The differential lambda-calculus. Theoretical Computer Science, 309(1), 1–41. https://doi.org/10/bf3b8v
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Ehrhard, T., & Tasson, C. (2018). Probabilistic call by push value. ArXiv:1607.04690 [Cs]. https://doi.org/10/ggdk8z
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Fages, F., Calzone, L., & Soliman, S. (2006). BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics, 22(14), 1805–1807. https://doi.org/10/dfv
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Fages, F., Martinez, T., Rosenblueth, D. A., & Soliman, S. (2018). Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 15(4), 1138–1151. https://doi.org/10/ggdf94
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Fiore, M., Gambino, N., Hyland, M., & Winskel, G. (2008). The cartesian closed bicategory of generalised species of structures. Journal of the London Mathematical Society, 77(1), 203–220. https://doi.org/10/bd2mr9
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Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452–459. https://doi.org/10/gdxwhq
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Heunen, C., Kammar, O., Staton, S., & Yang, H. (2017). A Convenient Category for Higher-Order Probability Theory. ArXiv:1701.02547 [Cs, Math]. Retrieved from http://arxiv.org/abs/1701.02547
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Hur, C.-K., Nori, A. V., & Rajamani, S. K. (2015). A Provably Correct Sampler for Probabilistic Programs, 21.
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Jacobs, B., & Zanasi, F. (2017). A Formal Semantics of Influence in Bayesian Reasoning. Schloss Dagstuhl - Leibniz-Zentrum Fuer Informatik GmbH, Wadern/Saarbruecken, Germany. https://doi.org/10/ggdgbc
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Jacobs, B., & Zanasi, F. (2016). A Predicate/State Transformer Semantics for Bayesian Learning. Electronic Notes in Theoretical Computer Science, 325, 185–200. https://doi.org/10/ggdgbb
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Keimel, K., & Plotkin, G. D. (2017). Mixed powerdomains for probability and nondeterminism. ArXiv:1612.01005 [Cs]. https://doi.org/10/ggdmrp
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