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 | 154 | 2016 |
Symbolic disintegration with a variety of base measures P Narayanan, C Shan ACM Transactions on Programming Languages and Systems (TOPLAS) 42 (2), 1-60, 2020 | 15 | 2020 |
From high-level inference algorithms to efficient code R Walia, P Narayanan, J Carette, S Tobin-Hochstadt, C Shan Proceedings of the ACM on Programming Languages 3 (ICFP), 1-30, 2019 | 15 | 2019 |
Symbolic conditioning of arrays in probabilistic programs P Narayanan, C Shan Proceedings of the ACM on Programming Languages 1 (ICFP), 1-25, 2017 | 12 | 2017 |
Applications of a disintegration transformation P Narayanan, C Shan Program Transformations for ML Workshop at NeurIPS 2019, 2019 | 4 | 2019 |
Graph algorithms in a guaranteeddeterministic language P Narayanan, RR Newton Workshop on Deterministic and Correctness in Parallel Programming (WoDet’14), 2014 | 3 | 2014 |
Verifiable and reusable conditioning P Narayanan Indiana University, 2019 | 2 | 2019 |
A combinator library for MCMC sampling P Narayanan, C Shan 3rd NIPS Workshop on Probabilistic Programming, 2014 | 2 | 2014 |
Efficient compilation of array probabilistic programs R Walia, J Carette, P Narayanan, C Shan, S Tobin-Hochstadt arXiv preprint arXiv:1805.06562, 2018 | 1 | 2018 |
Building blocks for exact and approximate inference J Carette, P Narayanan, W Romano, C Shan, R Zinkov | | |
More support for symbolic disintegration P Narayanan, C Shan | | |