| 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 | 213 | 2017 |
| 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 European Conference on Computer Vision (ECCV), 2024 | 149 | 2024 |
| Understanding failures in out-of-distribution detection with deep generative models L Zhang*, M Goldstein, R Ranganath International Conference on Machine Learning (ICML), 12427-12436, 2021 | 125 | 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 | 66 | 2017 |
| X-CAL: Explicit Calibration for Survival Analysis M Goldstein*, X Han*, A Puli*, AJ Perotte, R Ranganath Advances in Neural Information Processing Systems (NeurIPS) 2020 33, 2020 | 44 | 2020 |
| Where to diffuse, how to diffuse, and how to get back: Automated learning for multivariate diffusions R Singhal*, M Goldstein*, R Ranganath International Conference on Learning Representations (ICLR), 2023 | 21 | 2023 |
| Stochastic interpolants with data-dependent couplings MS Albergo*, M Goldstein*, NM Boffi, R Ranganath, E Vanden-Eijnden International Conference on Machine Learning (ICML), 2023 | 20 | 2023 |
| Inverse-weighted survival games X Han*, M Goldstein*, A Puli, T Wies, A Perotte, R Ranganath Advances in neural information processing systems (NeurIPS) 34, 2160-2172, 2021 | 14* | 2021 |
| Probabilistic Forecasting with Stochastic Interpolants and F\" ollmer Processes Y Chen*, M Goldstein*, M Hua*, MS Albergo, NM Boffi, E Vanden-Eijnden International Conference on Machine Learning (ICML), 2024 | 11 | 2024 |
| Survival mixture density networks X Han*, M Goldstein, R Ranganath Machine Learning for Healthcare Conference (MLHC), 224-248, 2022 | 11 | 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 | 8* | 2024 |
| Learning invariant representations with missing data M Goldstein*, JH Jacobsen, O Chau, A Saporta, AM Puli, R Ranganath, ... Conference on Causal Learning and Reasoning (CLeaR), 290-301, 2022 | 8 | 2022 |
| What's the score? Automated Denoising Score Matching for Nonlinear Diffusions R Singhal*, M Goldstein*, R Ranganath International Conference on Machine Learning (ICML), 2024 | 4 | 2024 |
| Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities A Saporta*, A Puli, M Goldstein, R Ranganath Advances in neural information processing systems (NeurIPS), 2024 | 1 | 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 |
| Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do Y Wald, M Goldstein, Y Efroni, WAC van Amsterdam, R Ranganath arXiv preprint arXiv:2503.15890, 2025 | | 2025 |
| Time After Time: Scalable Effect Estimation for Interventions on When and What to do Y Wald, Y Efroni, M Goldstein, WAC van Amsterdam, R Ranganath The Thirteenth International Conference on Learning Representations, 0 | | |
| Symile-MIMIC: a multimodal clinical dataset of chest X-rays, electrocardiograms, and blood labs from MIMIC-IV A Saporta, AM Puli, M Goldstein, R Ranganath | | |
| GATO: Gates Are Not the Only Option M Goldstein*, X Han*, R Ranganath | | |
Rajesh RanganathAssistant Professor, NYUVerified email at cs.princeton.edu
