Adam Ścibior
Cited by
Cited by
Denotational validation of higher-order Bayesian inference
A Ścibior, O Kammar, M Vákár, S Staton, H Yang, Y Cai, K Ostermann, ...
arXiv preprint arXiv:1711.03219, 2017
Practical probabilistic programming with monads
A Ścibior, Z Ghahramani, AD Gordon
Proceedings of the 2015 ACM SIGPLAN Symposium on Haskell, 165-176, 2015
Functional programming for modular Bayesian inference
A Ścibior, O Kammar, Z Ghahramani
Proceedings of the ACM on Programming Languages 2 (ICFP), 1-29, 2018
Planning as Inference in Epidemiological Dynamics Models
F Wood, A Warrington, S Naderiparizi, C Weilbach, V Masrani, W Harvey, ...
Frontiers in Artificial Intelligence 4, 550603, 2022
Robust asymmetric learning in pomdps
A Warrington, JW Lavington, A Scibior, M Schmidt, F Wood
International Conference on Machine Learning, 11013-11023, 2021
Imagining the road ahead: Multi-agent trajectory prediction via differentiable simulation
A Ścibior, V Lioutas, D Reda, P Bateni, F Wood
2021 IEEE International Intelligent Transportation Systems Conference (ITSC …, 2021
Deep probabilistic surrogate networks for universal simulator approximation
A Munk, A Scibior, AG Baydin, A Stewart, G Fernlund, A Poursartip, ...
arXiv preprint arXiv:1910.11950 25, 2019
Consistent kernel mean estimation for functions of random variables
CJ Simon-Gabriel, A Scibior, IO Tolstikhin, B Schölkopf
Advances in Neural Information Processing Systems 29, 2016
The semantic structure of quasi-Borel spaces
C Heunen, O Kammar, S Staton, S Moss, M Vákár, A Ścibior, H Yang
PPS Workshop on Probabilistic Programming Semantics, 2018
Differentiable particle filtering without modifying the forward pass
A Ścibior, F Wood
arXiv preprint arXiv:2106.10314, 2021
Fabular: Regression formulas as probabilistic programming
J Borgström, AD Gordon, L Ouyang, C Russo, A Ścibior, M Szymczak
Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of …, 2016
Amortized rejection sampling in universal probabilistic programming
S Naderiparizi, A Scibior, A Munk, M Ghadiri, AG Baydin, ...
International Conference on Artificial Intelligence and Statistics, 8392-8412, 2022
Semi-supervised sequential generative models
M Teng, TA Le, A Scibior, F Wood
arXiv preprint arXiv:2007.00155, 2020
Effects in Bayesian inference
A Scibior, O Kammar
Workshop on Higher-Order Programming with Effects (HOPE), 2015
Probabilistic surrogate networks for simulators with unbounded randomness
A Munk, B Zwartsenberg, A Ścibior, AGG Baydin, A Stewart, G Fernlund, ...
Uncertainty in Artificial Intelligence, 1423-1433, 2022
The Turing language for probabilistic programming
H Ge, K Xu, A Scibior, Z Ghahramani
The Turing language for probabilistic programming, 2016
Modular construction of Bayesian inference algorithms
A Scibior, Z Ghahramani
NIPS Workshop on Advances in Approximate Bayesian Inference, 2016
Efficient Bayesian inference for nested simulators
B Gram-Hansen, CS de Witt, R Zinkov, S Naderiparizi, A Scibior, A Munk, ...
Second Symposium on Advances in Approximate Bayesian Inference, 2019
Formally justified and modular Bayesian inference for probabilistic programs
AM Ścibior
University of Cambridge, 2019
Strongly typed tracing of probabilistic programs
A Ścibior, M Thomas
LAFI, 2019
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