| PyMC: a modern, and comprehensive probabilistic programming framework in Python O Abril-Pla, V Andreani, C Carroll, L Dong, CJ Fonnesbeck, M Kochurov, ... PeerJ Computer Science 9, e1516, 2023 | 240 | 2023 |
| Probabilistic inference by program transformation in Hakaru (system description) P Narayanan, J Carette, W Romano, C Shan, R Zinkov Functional and Logic Programming: 13th International Symposium, FLOPS 2016 …, 2016 | 160 | 2016 |
| 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 | 142 | 2017 |
| Potential-based Shaping in Model-based Reinforcement Learning. J Asmuth, ML Littman, R Zinkov AAAI, 604-609, 2008 | 113 | 2008 |
| Mask wearing in community settings reduces SARS-CoV-2 transmission G Leech, C Rogers-Smith, JT Monrad, JB Sandbrink, B Snodin, R Zinkov, ... Proceedings of the National Academy of Sciences 119 (23), e2119266119, 2022 | 99 | 2022 |
| Faithful inversion of generative models for effective amortized inference S Webb, A Golinski, R Zinkov, T Rainforth, YW Teh, F Wood Advances in Neural Information Processing Systems 31, 2018 | 53 | 2018 |
| 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 | 47 | 2018 |
| Composing inference algorithms as program transformations R Zinkov, C Shan Proceedings of Uncertainty in Artificial Intelligence, http://auai.org …, 2017 | 29 | 2017 |
| 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 | 20 | 2017 |
| Blackjax: Composable bayesian inference in jax A Cabezas, A Corenflos, J Lao, R Louf, A Carnec, K Chaudhari, ... arXiv preprint arXiv:2402.10797, 2024 | 16 | 2024 |
| Mass mask-wearing notably reduces COVID-19 transmission G Leech, C Rogers-Smith, JB Sandbrink, B Snodin, R Zinkov, B Rader, ... MedRxiv, 2021.06. 16.21258817, 2021 | 16 | 2021 |
| Verified multi-step synthesis using large language models and monte Carlo tree search D Brandfonbrener, S Raja, T Prasad, C Loughridge, J Yang, S Henniger, ... arXiv e-prints, arXiv: 2402.08147, 2024 | 10 | 2024 |
| Pymc: A modern and comprehensive probabilistic programming framework in python. PeerJ Computer Science 9: e1516 AP Oriol, A Virgile, C Colin, D Larry, MK J FC, K Ravin, L Jupeng, ... | 10 | 2023 |
| 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 | 7 | 2022 |
| Simulation-based inference for global health decisions CS de Witt, B Gram-Hansen, N Nardelli, A Gambardella, R Zinkov, ... arXiv preprint arXiv:2005.07062, 2020 | 7 | 2020 |
| VerMCTS: Synthesizing Multi-Step Programs using a Verifier, a Large Language Model, and Tree Search D Brandfonbrener, S Henniger, S Raja, T Prasad, C Loughridge, ... arXiv preprint arXiv:2402.08147, 2024 | 5 | 2024 |
| 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 | 5 | 2019 |
| 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 | 4 | 2009 |
| probKanren: A Simple Probabilistic Extension for microKanren. R Zinkov, WE Byrd ICLP Workshops, 2021 | 1 | 2021 |
| Simulation-based inference for global health decisions C Schroeder de Witt, B Gram-Hansen, N Nardelli, A Gambardella, ... | | 2020 |
