A type theory for probability density functions, Principles of Programming Languages (POPL, pp.545-556, 2012. ,
Deriving Probability Density Functions from Probabilistic Functional Programs, Tools and Algorithms for the Construction and Analysis of Systems (TACAS), pp.508-522, 2013. ,
Pyro: Deep Universal Probabilistic Programming, Journal of Machine Learning Research, vol.20, pp.1-6, 2019. ,
A lambda-calculus foundation for universal probabilistic programming, International Conference on Functional Programming (ICFP), pp.33-46, 2016. ,
Importance Weighted Autoencoders, International Conference on Learning Representations (ICLR), 2016. ,
Stan: A Probabilistic Programming Language, Journal of Statistical Software, vol.76, pp.1-32, 2017. ,
Efficiently Sampling Probabilistic Programs via Program Analysis, Artificial Intelligence and Statistics (AISTATS), pp.153-160, 2013. ,
Probabilistic Program Analysis with Martingales, Computer Aided Verification (CAV), pp.511-526, 2013. ,
Continuity analysis of programs, Principles of Programming Languages (POPL, pp.57-70, 2010. ,
Abstract Interpretation: A Unified Lattice Model for Static Analysis of Programs by Construction or Approximation of Fixpoints, Principles of Programming Languages (POPL, pp.238-252, 1977. ,
Systematic design of program analysis frameworks, Principles of Programming Languages (POPL, pp.269-282, 1979. ,
Abstract Interpretation Frameworks, Journal of Logic and Computation, vol.2, issue.4, pp.511-547, 1992. ,
Probabilistic Abstract Interpretation, European Symposium on Programming (ESOP), pp.169-193, 2012. ,
Probabilistic coherence spaces are fully abstract for probabilistic PCF, Principles of Programming Languages (POPL, pp.309-320, 2014. ,
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models, Neural Information Processing Systems (NIPS), pp.3233-3241, 2016. ,
PSI: Exact Symbolic Inference for Probabilistic Programs, Computer Aided Verification (CAV, pp.62-83, 2016. ,
Introduction to Markov Chain Monte Carlo, pp.3-48, 2011. ,
Existence and Continuity of Differential Entropy for a Class of Distributions, IEEE Communications Letters, vol.21, pp.1469-1472, 2017. ,
Church: a language for generative models, Uncertainty in Artificial Intelligence (UAI), pp.220-229, 2008. ,
Tabular: A Schema-driven Probabilistic Programming Language, Principles of Programming Languages (POPL, pp.321-334, 2014. ,
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, pp.711-732, 1995. ,
Monte Carlo Sampling Methods Using Markov Chains and Their Applications, Biometrika, vol.57, pp.97-109, 1970. ,
A convenient category for higher-order probability theory, Logic in Computer Science (LICS, pp.1-12, 2017. ,
Stochastic Variational Inference, Journal of Machine Learning Research, vol.14, pp.1303-1347, 2013. ,
A Provably Correct Sampler for Probabilistic Programs, Foundation of Software Technology and Theoretical Computer Science (FSTTCS, pp.475-488, 2015. ,
A Probabilistic Powerdomain of Evaluations, Logic in Computer Science (LICS), pp.186-195, 1989. ,
Semi-supervised Learning with Deep Generative Models, Neural Information Processing Systems (NIPS), pp.3581-3589, 2014. ,
Auto-Encoding Variational Bayes, International Conference on Learning Representations (ICLR), 2014. ,
Probabilistic Programming Language and its Incremental Evaluation, Asian Symposium on Programming Languages and Systems (APLAS), pp.357-376, 2016. ,
, Probability Theory: A Comprehensive Course, 2014.
Semantics of Probabilistic Programs, J. Comput. System Sci, vol.22, pp.328-350, 1981. ,
Structured Inference Networks for Nonlinear State Space Models, AAAI Conference on Artificial Intelligence (AAAI, pp.2101-2109, 2017. ,
Automatic Variational Inference in Stan, Neural Information Processing Systems (NIPS), pp.568-576, 2015. ,
Automatic Differentiation Variational Inference, Journal of Machine Learning Research, vol.18, p.45, 2017. ,
Inference Compilation and Universal Probabilistic Programming, In Artificial Intelligence and Statistics, pp.1338-1348, 2017. ,
, Towards Verified Stochastic Variational Inference for Probabilistic Programs, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02399922
Venture: a higher-order probabilistic programming platform with programmable inference, 2014. ,
Equation of State Calculations by Fast Computing Machines, The Journal of Chemical Physics, vol.21, pp.1087-1092, 1953. ,
, Infer.NET 2.6. Microsoft Research Cambridge, 2014.
Differentiable Abstract Interpretation for Provably Robust Neural Networks, International Conference on Machine Learning (ICML), pp.3575-3583, 2018. ,
Abstract Interpretation of Probabilistic Semantics, Static Analysis Symposium (SAS), pp.322-339, 2000. ,
Backwards Abstract Interpretation of Probabilistic Programs, European Symposium on Programming (ESOP), pp.367-382, 2001. ,
, On Entropy for Mixtures of Discrete and Continuous Variables, 2006.
Probabilistic inference by program transformation in Hakaru (system description), Functional and Logic Programming (FLOPS), pp.62-79, 2016. ,
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants, Learning in Graphical Models, pp.355-368, 1998. ,
R2: An Efficient MCMC Sampler for Probabilistic Programs, AAAI Conference on Artificial Intelligence (AAAI, pp.2476-2482, 2014. ,
Variational Bayesian Inference with Stochastic Search, International Conference on Machine Learning (ICML), pp.1363-1370, 2012. ,
Black Box Variational Inference, Artificial Intelligence and Statistics (AISTATS), pp.814-822, 2014. ,
Deep Exponential Families, Artificial Intelligence and Statistics (AISTATS), pp.762-771, 2015. ,
Denotational validation of higher-order Bayesian inference, POPL, vol.2, p.29, 2018. ,
Learning Disentangled Representations with Semi-Supervised Deep Generative Models, Neural Information Processing Systems (NIPS), pp.5927-5937, 2017. ,
Cantor meets scott: semantic foundations for probabilistic networks, Principles of Programming Languages (POPL, pp.557-571, 2017. ,
Autoencoding Variational Inference For Topic Models, International Conference on Learning Representations (ICLR), 2017. ,
Commutative Semantics for Probabilistic Programming, European Symposium on Programming (ESOP), pp.855-879, 2017. ,
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints, Logic in Computer Science (LICS, pp.525-534, 2016. ,
Running Probabilistic Programs Backwards, European Symposium on Programming (ESOP), pp.53-79, 2015. ,
Simple, Distributed, and Accelerated Probabilistic Programming, Neural Information Processing Systems (NeurIPS), pp.7609-7620, 2018. ,
, A library for probabilistic modeling, inference, and criticism, 2016.
Pyro examples, 2019. ,
Pyro regression test suite, 2019. ,
A domain theory for statistical probabilistic programming, PACMPL, vol.3, p.29, 2019. ,
Black-Box Policy Search with Probabilistic Programs, In Artificial Intelligence and Statistics, pp.1195-1204, 2016. ,
PMAF: an algebraic framework for static analysis of probabilistic programs, Programming Language Design and Implementation (PLDI, pp.513-528, 2018. ,
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning, Machine Learning, vol.8, pp.229-256, 1992. ,
Automated Variational Inference in Probabilistic Programming, 2013. ,
A New Approach to Probabilistic Programming Inference, Artificial Intelligence and Statistics (AISTATS), pp.1024-1032, 2014. ,
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms, International Conference on Machine Learning (ICML), pp.5339-5348, 2018. ,
Implementing Inference Algorithms for Probabilistic Programs, 2019. ,