| The Blessings of Multiple Causes Y Wang, DM Blei Journal of the American Statistical Association 114 (528), 1574-1596, 2019 | 282 | 2019 |
| Frequentist Consistency of Variational Bayes Y Wang, DM Blei Journal of the American Statistical Association 114 (527), 1147-1161, 2019 | 226 | 2019 |
| Causal inference for recommender systems Y Wang, D Liang, L Charlin, DM Blei Proceedings of the 14th ACM Conference on Recommender Systems, 426-431, 2020 | 167* | 2020 |
| Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations Y Wang, JR Zubizarreta Biometrika 107 (1), 93-105, 2020 | 117 | 2020 |
| Robust Probabilistic Modeling with Bayesian Data Reweighting Y Wang, A Kucukelbir, DM Blei International Conference on Machine Learning, 3646-3655, 2017 | 101 | 2017 |
| Using embeddings to correct for unobserved confounding in networks V Veitch, Y Wang, DM Blei Advances in Neural Information Processing Systems, 2019 | 62* | 2019 |
| Posterior Collapse and Latent Variable Non-identifiability Y Wang, DM Blei, JP Cunningham Advances in Neural Information Processing Systems, 2021 | 47 | 2021 |
| Variational Bayes under Model Misspecification Y Wang, DM Blei Advances in Neural Information Processing Systems, 2019 | 47 | 2019 |
| Desiderata for representation learning: A causal perspective Y Wang, MI Jordan arXiv preprint arXiv:2109.03795, 2021 | 45 | 2021 |
| Identifiable deep generative models via sparse decoding GE Moran, D Sridhar, Y Wang, D Blei Transactions on Machine Learning Research, 2022 | 39* | 2022 |
| Instrumental variable value iteration for causal offline reinforcement learning L Liao, Z Fu, Z Yang, Y Wang, M Kolar, Z Wang arXiv preprint arXiv:2102.09907, 2021 | 36 | 2021 |
| Interventional causal representation learning K Ahuja, D Mahajan, Y Wang, Y Bengio International conference on machine learning, 372-407, 2023 | 31 | 2023 |
| Learning Equilibria in Matching Markets with Bandit Feedback M Jagadeesan, A Wei, Y Wang, MI Jordan, J Steinhardt Journal of the ACM 70 (3), 1-46, 2023 | 25 | 2023 |
| Conformal Sensitivity Analysis for Individual Treatment Effects M Yin, C Shi, Y Wang, DM Blei Journal of the American Statistical Association, 1-30, 2022 | 24 | 2022 |
| Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models Y Wang, A Degleris, AH Williams, SW Linderman Journal of American Statistical Association, 2023 | 23* | 2023 |
| Point process models for sequence detection in high-dimensional neural spike trains AH Williams, A Degleris, Y Wang, SW Linderman Advances in Neural Information Processing Systems, 2020 | 23 | 2020 |
| A Proxy Variable View of Shared Confounding Y Wang, DM Blei International Conference on Machine Learning, 10697-10707, 2021 | 22* | 2021 |
| Black Box FDR W Tansey, Y Wang, DM Blei, R Rabadan International Conference on Machine Learning, 4874-4883, 2018 | 21 | 2018 |
| The medical deconfounder: assessing treatment effects with electronic health records L Zhang, Y Wang, A Ostropolets, JJ Mulgrave, DM Blei, G Hripcsak Machine Learning for Healthcare Conference, 490-512, 2019 | 20 | 2019 |
| Towards clarifying the theory of the deconfounder Y Wang, DM Blei arXiv preprint arXiv:2003.04948, 2020 | 19 | 2020 |
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
