| Pyro: Deep universal probabilistic programming E Bingham, JP Chen, M Jankowiak, F Obermeyer, N Pradhan, ... The Journal of Machine Learning Research 20 (1), 973-978, 2019 | 1351 | 2019 |
| Variational Bayesian optimal experimental design A Foster, M Jankowiak, E Bingham, P Horsfall, YW Teh, T Rainforth, ... Advances in Neural Information Processing Systems 32, 2019 | 142 | 2019 |
| Tensor variable elimination for plated factor graphs F Obermeyer, E Bingham, M Jankowiak, N Pradhan, J Chiu, A Rush, ... International Conference on Machine Learning, 4871-4880, 2019 | 23 | 2019 |
| Functional Tensors for Probabilistic Programming F Obermeyer, E Bingham, M Jankowiak, D Phan, JP Chen arXiv preprint arXiv:1910.10775, 2019 | 22 | 2019 |
| Pyro-Velocity: Probabilistic RNA Velocity inference from single-cell data Q Qin, E Bingham, G La Manno, DM Langenau, L Pinello bioRxiv, 2022.09. 12.507691, 2022 | 10 | 2022 |
| Transpiling Stan models to Pyro JP Chen, R Singh, E Bingham, N Goodman The International Conference on Probabilistic Programming (PROBPROG), 2018 | 3 | 2018 |
| Pyro: Deep probabilistic programming B Eli, JP Chen, M Jankowiak, T Karaletsos, F Obermeyer, N Pradhan, ... | 3 | 2017 |
| Automated Efficient Estimation using Monte Carlo Efficient Influence Functions R Agrawal, S Witty, A Zane, E Bingham arXiv preprint arXiv:2403.00158, 2024 | 1 | 2024 |
| Leveraging conditional independence in Pyro E Bingham, F Obermeyer, M Jankowiak, N Pradhan, N Goodman The International Conference on Probabilistic Programming (PROBPROG), 2018 | | 2018 |
| Automated enumeration of discrete latent variables F Obermeyer, E Bingham, M Jankowiak, N Pradhan, N Goodman The International Conference on Probabilistic Programming (PROBPROG), 2018 | | 2018 |
| Characterizing how Visual Question Answering models scale with the world E Bingham, P Molino, P Szerlip, F Obermeyer, ND Goodman | | |
