Rob Zinkov
TitleCited byYear
Potential-based Shaping in Model-based Reinforcement Learning.
J Asmuth, ML Littman, R Zinkov
AAAI, 604-609, 2008
762008
Probabilistic inference by program transformation in Hakaru (system description)
P Narayanan, J Carette, W Romano, C Shan, R Zinkov
International Symposium on Functional and Logic Programming, 62-79, 2016
512016
Using synthetic data to train neural networks is model-based reasoning
TA Le, AG Baydin, R Zinkov, F Wood
2017 International Joint Conference on Neural Networks (IJCNN), 3514-3521, 2017
342017
Composing inference algorithms as program transformations
R Zinkov, C Shan
Proceedings of Uncertainty in Artificial Intelligence, http://auai.org …, 2017
192017
Faithful inversion of generative models for effective amortized inference
S Webb, A Golinski, R Zinkov, N Siddharth, T Rainforth, YW Teh, F Wood
Advances in Neural Information Processing Systems, 3070-3080, 2018
92018
Querying word embeddings for similarity and relatedness
FT Asr, R Zinkov, M Jones
Proceedings of the 2018 Conference of the North American Chapter of the …, 2018
72018
End-to-end training of differentiable pipelines across machine learning frameworks
M Milutinovic, AG Baydin, R Zinkov, W Harvey, D Song, F Wood, W Shen
72017
Sensitivity analysis for distributed optimization with resource constraints
E Bowring, Z Yin, R Zinkov, M Tambe
Proceedings of The 8th International Conference on Autonomous Agents and …, 2009
42009
Amortized rejection sampling in universal probabilistic programming
S Naderiparizi, A Ścibior, A Munk, M Ghadiri, AG Baydin, B Gram-Hansen, ...
arXiv preprint arXiv:1910.09056, 2019
12019
Hasty-A generative Model Complier
F Wood, M Teng, R Zinkov
University of Oxford Oxford United Kingdom, 2019
2019
Automating Expectation Maximixation
R Zinkov
Building blocks for exact and approximate inference
J Carette, P Narayanan, W Romano, C Shan, R Zinkov
Efficient Probabilistic Programming Languages
R Zinkov
Probabilistic Programming in R with Bruno
R Zinkov, CC Shan
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Articles 1–14