| Variational autoencoders for collaborative filtering D Liang, RG Krishnan, MD Hoffman, T Jebara Proceedings of the 2018 World Wide Web Conference, 689-698, 2018 | 1800 | 2018 |
| Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning RJ Chen, C Chen, Y Li, TY Chen, AD Trister, RG Krishnan, F Mahmood Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 791 | 2022 |
| Structured Inference Networks for Nonlinear State Space Models RG Krishnan, U Shalit, D Sontag arXiv preprint arXiv:1609.09869, 2016 | 629 | 2016 |
| Deep Kalman Filters RG Krishnan, U Shalit, D Sontag arXiv preprint arXiv:1511.05121, 2015 | 501 | 2015 |
| Clinical camel: An open expert-level medical language model with dialogue-based knowledge encoding A Toma, PR Lawler, J Ba, RG Krishnan, BB Rubin, B Wang arXiv preprint arXiv:2305.12031, 2023 | 179 | 2023 |
| Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology RJ Chen, RG Krishnan arXiv preprint arXiv:2203.00585, 2022 | 112 | 2022 |
| On the challenges of learning with inference networks on sparse, high-dimensional data RG Krishnan, D Liang, MD Hoffman The 21st International Conference on Artificial Intelligence and Statistics, 2018 | 112 | 2018 |
| Performance of Large Language Models on Medical Oncology Examination Questions JB Longwell, I Hirsch, F Binder, GAG Conchas, D Mau, R Jang, ... JAMA Network Open 7 (6), e2417641-e2417641, 2024 | 70 | 2024 |
| Machine learning in computational histopathology: Challenges and opportunities M Cooper, Z Ji, RG Krishnan Genes, Chromosomes and Cancer 62 (9), 540-556, 2023 | 55 | 2023 |
| Barrier Frank-Wolfe for marginal inference RG Krishnan, S Lacoste-Julien, D Sontag Advances in Neural Information Processing Systems, 532-540, 2015 | 50 | 2015 |
| Representation learning approaches to detect false arrhythmia alarms from ecg dynamics EP Lehman, RG Krishnan, X Zhao, RG Mark, LH Lehman Machine learning for healthcare conference, 571-586, 2018 | 36 | 2018 |
| Clustering Interval-Censored Time-Series for Disease Phenotyping IY Chen, RG Krishnan, D Sontag Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6211-6221, 2022 | 27 | 2022 |
| A Learning Based Hypothesis Test for Harmful Covariate Shift T Ginsberg, Z Liang, RG Krishnan arXiv preprint arXiv:2212.02742, 2022 | 23 | 2022 |
| End-to-end causal effect estimation from unstructured natural language data N Dhawan, L Cotta, K Ullrich, RG Krishnan, CJ Maddison Advances in Neural Information Processing Systems 37, 77165-77199, 2025 | 21 | 2025 |
| Early detection of diabetes from health claims R Krishnan, N Razavian, Y Choi, S Nigam, S Blecker, A Schmidt, ... Machine Learning in Healthcare Workshop, NIPS, 1-5, 2013 | 21 | 2013 |
| Duett: Dual event time transformer for electronic health records A Labach, A Pokhrel, XS Huang, S Zuberi, SE Yi, M Volkovs, T Poutanen, ... Machine Learning for Healthcare Conference, 403-422, 2023 | 20 | 2023 |
| Partial identification of treatment effects with implicit generative models V Balazadeh Meresht, V Syrgkanis, RG Krishnan Advances in Neural Information Processing Systems 35, 22816-22829, 2022 | 20* | 2022 |
| Neural pharmacodynamic state space modeling ZM Hussain, RG Krishnan, D Sontag International Conference on Machine Learning, 4500-4510, 2021 | 20 | 2021 |
| Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge MR Rezaei, RS Fard, JL Parker, RG Krishnan, M Lankarany arXiv preprint arXiv:2502.13010, 2025 | 18* | 2025 |
| A Geometric Explanation of the Likelihood OOD Detection Paradox H Kamkari, BL Ross, JC Cresswell, AL Caterini, RG Krishnan, ... arXiv preprint arXiv:2403.18910, 2024 | 16 | 2024 |
David SontagProfessor, Massachusetts Institute of TechnologyVerified email at csail.mit.edu
