| Semi-amortized variational autoencoders Y Kim, S Wiseman, A Miller, D Sontag, A Rush International Conference on Machine Learning, 2678-2687, 2018 | 193 | 2018 |
| Advances in nowcasting influenza-like illness rates using search query logs V Lampos, AC Miller, S Crossan, C Stefansen Scientific reports 5 (1), 1-10, 2015 | 136 | 2015 |
| Bayesian learning and inference in recurrent switching linear dynamical systems S Linderman, M Johnson, A Miller, R Adams, D Blei, L Paninski Artificial Intelligence and Statistics, 914-922, 2017 | 134 | 2017 |
| Factorized point process intensities: A spatial analysis of professional basketball A Miller, L Bornn, R Adams, K Goldsberry International conference on machine learning, 235-243, 2014 | 125 | 2014 |
| Characterizing the spatial structure of defensive skill in professional basketball A Franks, A Miller, L Bornn, K Goldsberry The Annals of Applied Statistics 9 (1), 94-121, 2015 | 109 | 2015 |
| Variational boosting: Iteratively refining posterior approximations AC Miller, NJ Foti, RP Adams International Conference on Machine Learning, 2420-2429, 2017 | 101 | 2017 |
| Counterpoints: Advanced defensive metrics for nba basketball A Franks, A Miller, L Bornn, K Goldsberry 9th annual MIT sloan sports analytics conference, Boston, MA, 2015 | 77 | 2015 |
| Reducing reparameterization gradient variance A Miller, N Foti, A D'Amour, RP Adams Advances in Neural Information Processing Systems 30, 2017 | 62 | 2017 |
| Probabilistic models of larval zebrafish behavior reveal structure on many scales RE Johnson, S Linderman, T Panier, CL Wee, E Song, KJ Herrera, ... Current Biology 30 (1), 70-82. e4, 2020 | 55 | 2020 |
| Recurrent switching linear dynamical systems SW Linderman, AC Miller, RP Adams, DM Blei, L Paninski, MJ Johnson arXiv preprint arXiv:1610.08466, 2016 | 43 | 2016 |
| Possession sketches: Mapping nba strategies AC Miller, L Bornn Proceedings of the MIT Sloan Sports Analytics Conference, Boston, MA, USA, 3-4, 2017 | 40 | 2017 |
| Celeste: Variational inference for a generative model of astronomical images J Regier, A Miller, J McAuliffe, R Adams, M Hoffman, D Lang, D Schlegel, ... International Conference on Machine Learning, 2095-2103, 2015 | 34 | 2015 |
| Real-time rendering and dynamic updating of 3-d volumetric data A Miller, V Jain, JL Mundy Proceedings of the Fourth Workshop on General Purpose Processing on Graphics …, 2011 | 33 | 2011 |
| Mobility trends provide a leading indicator of changes in SARS-CoV-2 transmission AC Miller, NJ Foti, JA Lewnard, NP Jewell, C Guestrin, EB Fox MedRxiv, 2020 | 29 | 2020 |
| Mitigation of SARS-CoV-2 transmission at a large public university DRE Ranoa, RL Holland, FG Alnaji, KJ Green, L Wang, RL Fredrickson, ... medRxiv, 2021 | 8 | 2021 |
| Probabilistic models of larval zebrafish behavior: structure on many scales RE Johnson, S Linderman, T Panier, CL Wee, E Song, KJ Herrera, ... bioRxiv, 672246, 2019 | 6 | 2019 |
| Breiman's two cultures: You don't have to choose sides AC Miller, NJ Foti, EB Fox Observational Studies 7 (1), 161-169, 2021 | 5 | 2021 |
| Studying basketball through the lens of player tracking data L Bornn, D Cervone, A Franks, A Miller Handbook of statistical methods and analyses in sports, 261-286, 2017 | 5 | 2017 |
| Learning insulin-glucose dynamics in the wild AC Miller, NJ Foti, E Fox Machine Learning for Healthcare Conference, 172-197, 2020 | 4 | 2020 |
| Statistical deconvolution for inference of infection time series AC Miller, L Hannah, J Futoma, NJ Foti, EB Fox, A D’Amour, M Sandler, ... medRxiv, 2020 | 4 | 2020 |
