| Practical whole-system provenance capture T Pasquier, X Han, M Goldstein, T Moyer, D Eyers, M Seltzer, J Bacon Proceedings of the 2017 Symposium on Cloud Computing, 405-418, 2017 | 172 | 2017 |
| Understanding failures in out-of-distribution detection with deep generative models L Zhang, M Goldstein, R Ranganath International Conference on Machine Learning, 12427-12436, 2021 | 94 | 2021 |
| {FRAPpuccino}: Fault-detection through Runtime Analysis of Provenance X Han, T Pasquier, T Ranjan, M Goldstein, M Seltzer 9th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 17), 2017 | 55 | 2017 |
| X-CAL: Explicit Calibration for Survival Analysis M Goldstein, X Han, A Puli, AJ Perotte, R Ranganath Advances in Neural Information Processing Systems 2020 33, 2020 | 35 | 2020 |
| SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers N Ma, M Goldstein, MS Albergo, NM Boffi, E Vanden-Eijnden, S Xie arXiv preprint arXiv:2401.08740, 2024 | 22 | 2024 |
| Where to diffuse, how to diffuse, and how to get back: Automated learning for multivariate diffusions R Singhal, M Goldstein, R Ranganath arXiv preprint arXiv:2302.07261, 2023 | 15 | 2023 |
| Inverse-weighted survival games X Han, M Goldstein, A Puli, T Wies, A Perotte, R Ranganath Advances in neural information processing systems 34, 2160-2172, 2021 | 11* | 2021 |
| Stochastic interpolants with data-dependent couplings MS Albergo, M Goldstein, NM Boffi, R Ranganath, E Vanden-Eijnden arXiv preprint arXiv:2310.03725, 2023 | 8 | 2023 |
| Learning invariant representations with missing data M Goldstein, JH Jacobsen, O Chau, A Saporta, AM Puli, R Ranganath, ... Conference on Causal Learning and Reasoning, 290-301, 2022 | 8 | 2022 |
| Survival mixture density networks X Han, M Goldstein, R Ranganath Machine Learning for Healthcare Conference, 224-248, 2022 | 5 | 2022 |
| Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning Y Hu, A Lui, M Goldstein, M Sudarshan, A Tinsay, C Tsui, SD Maidman, ... European Heart Journal: Acute Cardiovascular Care, zuae037, 2024 | 2 | 2024 |
| QTNet: Predicting Drug-Induced QT Prolongation With Artificial Intelligence–Enabled Electrocardiograms H Zhang, C Tarabanis, N Jethani, M Goldstein, S Smith, L Chinitz, ... Clinical Electrophysiology 10 (5), 956-966, 2024 | 1 | 2024 |
| Probabilistic Forecasting with Stochastic Interpolants and F\" ollmer Processes Y Chen, M Goldstein, M Hua, MS Albergo, NM Boffi, E Vanden-Eijnden arXiv preprint arXiv:2403.13724, 2024 | | 2024 |
| A dynamic risk score for early prediction of cardiogenic shock using machine learning Y Hu, A Lui, M Goldstein, M Sudarshan, A Tinsay, C Tsui, S Maidman, ... arXiv preprint arXiv:2303.12888, 2023 | | 2023 |
| GATO: Gates Are Not the Only Option M Goldstein, X Han, R Ranganath | | 2019 |
| What’s the score? Automated Denoising Score Matching for Nonlinear Diffusions R Singhal, M Goldstein, R Ranganath Forty-first International Conference on Machine Learning, 0 | | |
Rajesh RanganathAssistant Professor, NYU在 cs.princeton.edu 的电子邮件经过验证
