| Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations H Lee, R Grosse, R Ranganath, AY Ng Proceedings of the 26th annual international conference on machine learning …, 2009 | 3449 | 2009 |
| Black box variational inference R Ranganath, S Gerrish, D Blei Artificial intelligence and statistics, 814-822, 2014 | 1328 | 2014 |
| Automatic differentiation variational inference A Kucukelbir, D Tran, R Ranganath, A Gelman, DM Blei Journal of machine learning research 18 (14), 1-45, 2017 | 854 | 2017 |
| Clinicalbert: Modeling clinical notes and predicting hospital readmission K Huang, J Altosaar, R Ranganath arXiv preprint arXiv:1904.05342, 2019 | 835 | 2019 |
| Unsupervised learning of hierarchical representations with convolutional deep belief networks H Lee, R Grosse, R Ranganath, AY Ng Communications of the ACM 54 (10), 95-103, 2011 | 512 | 2011 |
| Hierarchical variational models R Ranganath, D Tran, D Blei International conference on machine learning, 324-333, 2016 | 376 | 2016 |
| Backprop kf: Learning discriminative deterministic state estimators T Haarnoja, A Ajay, S Levine, P Abbeel Advances in neural information processing systems 29, 2016 | 357* | 2016 |
| Hierarchical implicit models and likelihood-free variational inference D Tran, R Ranganath, D Blei Advances in Neural Information Processing Systems 30, 2017 | 351* | 2017 |
| A review of challenges and opportunities in machine learning for health M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath AMIA Summits on Translational Science Proceedings 2020, 191, 2020 | 304 | 2020 |
| Automatic variational inference in Stan A Kucukelbir, R Ranganath, A Gelman, D Blei Advances in neural information processing systems 28, 2015 | 301 | 2015 |
| Variational sequential monte carlo C Naesseth, S Linderman, R Ranganath, D Blei International conference on artificial intelligence and statistics, 968-977, 2018 | 242 | 2018 |
| Deep survival analysis R Ranganath, A Perotte, N Elhadad, D Blei Machine Learning for Healthcare Conference, 101-114, 2016 | 231 | 2016 |
| The variational Gaussian process D Tran, R Ranganath, DM Blei arXiv preprint arXiv:1511.06499, 2015 | 201 | 2015 |
| Support and invertibility in domain-invariant representations FD Johansson, D Sontag, R Ranganath arXiv preprint arXiv:1903.03448, 2019 | 190 | 2019 |
| Variational Inference via Upper Bound Minimization AB Dieng, D Tran, R Ranganath, J Paisley, D Blei Advances in Neural Information Processing Systems 30, 2017 | 179 | 2017 |
| The role of machine learning in clinical research: transforming the future of evidence generation EH Weissler, T Naumann, T Andersson, R Ranganath, O Elemento, Y Luo, ... Trials 22, 1-15, 2021 | 177 | 2021 |
| Reproducibility in machine learning for health research: Still a ways to go MBA McDermott, S Wang, N Marinsek, R Ranganath, L Foschini, ... Science Translational Medicine 13 (586), eabb1655, 2021 | 174 | 2021 |
| Deep exponential families R Ranganath, L Tang, L Charlin, D Blei Artificial intelligence and statistics, 762-771, 2015 | 158 | 2015 |
| Extracting social meaning: Identifying interactional style in spoken conversation D Jurafsky, R Ranganath, D McFarland Proceedings of Human Language Technologies: The 2009 Annual Conference of …, 2009 | 145 | 2009 |
| Offline rl without off-policy evaluation D Brandfonbrener, W Whitney, R Ranganath, J Bruna Advances in neural information processing systems 34, 4933-4946, 2021 | 144 | 2021 |
