| Variational inference: A review for statisticians DM Blei, A Kucukelbir, JD McAuliffe Journal of the American statistical Association 112 (518), 859-877, 2017 | 5957 | 2017 |
| Quantifying the local resolution of cryo-EM density maps A Kucukelbir, FJ Sigworth, HD Tagare Nature methods 11 (1), 63-65, 2014 | 1823 | 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 | 928 | 2017 |
| Edward: A library for probabilistic modeling, inference, and criticism D Tran, A Kucukelbir, AB Dieng, M Rudolph, D Liang, DM Blei arXiv preprint arXiv:1610.09787, 2016 | 368 | 2016 |
| Automatic variational inference in Stan A Kucukelbir, R Ranganath, A Gelman, D Blei Advances in neural information processing systems 28, 2015 | 306 | 2015 |
| Robust probabilistic modeling with Bayesian data reweighting Y Wang, A Kucukelbir, DM Blei Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 119 | 2017 |
| Directly reconstructing principal components of heterogeneous particles from cryo-EM images HD Tagare, A Kucukelbir, FJ Sigworth, H Wang, M Rao Journal of structural biology 191 (2), 245-262, 2015 | 78 | 2015 |
| A Bayesian adaptive basis algorithm for single particle reconstruction A Kucukelbir, FJ Sigworth, HD Tagare Journal of structural biology 179 (1), 56-67, 2012 | 25 | 2012 |
| Fully automatic variational inference of differentiable probability models A Kucukelbir, R Ranganath, A Gelman, D Blei NIPS Workshop on Probabilistic Programming, 69, 2014 | 21 | 2014 |
| Hindsight expectation maximization for goal-conditioned reinforcement learning Y Tang, A Kucukelbir International Conference on Artificial Intelligence and Statistics, 2863-2871, 2021 | 20 | 2021 |
| Variational deep Q network Y Tang, A Kucukelbir arXiv preprint arXiv:1711.11225, 2017 | 17 | 2017 |
| Population Empirical Bayes A Kucukelbir, DM Blei Uncertainty in Artificial Intelligence, 2015 | 16 | 2015 |
| Variance reduction for evolution strategies via structured control variates Y Tang, K Choromanski, A Kucukelbir International Conference on Artificial Intelligence and Statistics, 646-656, 2020 | 11 | 2020 |
| Evaluating Bayesian models with posterior dispersion indices A Kucukelbir, Y Wang, DM Blei International Conference on Machine Learning, 1925-1934, 2017 | 6 | 2017 |
| A new stochastic estimator for tremor frequency tracking A Kucukelbir, A Kushki, KN Plataniotis 2009 IEEE International Conference on Acoustics, Speech and Signal …, 2009 | 5 | 2009 |
| Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees N Beltran-Velez, AA Grande, A Nazaret, A Kucukelbir, D Blei arXiv preprint arXiv:2406.07658, 2024 | 2 | 2024 |
| Sparse and Steerable Representations for 3D Electron Cryomicroscopy A Kucukelbir Yale University, 2014 | 2 | 2014 |
| Artificial Intelligence for Climate Change Mitigation Roadmap DB Sandalow, C McCormick, A Kucukelbir, J Friedman, T Nagrani, Z Fan, ... | 1 | 2023 |
| Machine Learning and Climate A Kucukelbir, N Beltran Instructor, 2025 | | 2025 |
| PRINCIPAL CO-AUTHORS C McCormick, A Kucukelbir, J Friedmann, M Nachmany, H Lee, A Hill, ... | | 2024 |
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
