| Rapid learning or feature reuse? towards understanding the effectiveness of maml A Raghu, M Raghu, S Bengio, O Vinyals arXiv preprint arXiv:1909.09157, 2019 | 659 | 2019 |
| Continuous state-space models for optimal sepsis treatment: a deep reinforcement learning approach A Raghu, M Komorowski, LA Celi, P Szolovits, M Ghassemi Machine Learning for Healthcare Conference, 147-163, 2017 | 216 | 2017 |
| Deep reinforcement learning for sepsis treatment A Raghu, M Komorowski, I Ahmed, L Celi, P Szolovits, M Ghassemi arXiv preprint arXiv:1711.09602, 2017 | 190 | 2017 |
| Through-wall human mesh recovery using radio signals M Zhao, Y Liu, A Raghu, T Li, H Zhao, A Torralba, D Katabi Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 99 | 2019 |
| Representation balancing mdps for off-policy policy evaluation Y Liu, O Gottesman, A Raghu, M Komorowski, AA Faisal, F Doshi-Velez, ... Advances in neural information processing systems 31, 2018 | 74 | 2018 |
| Model-based reinforcement learning for sepsis treatment A Raghu, M Komorowski, S Singh arXiv preprint arXiv:1811.09602, 2018 | 46 | 2018 |
| Assessment of medication self-administration using artificial intelligence M Zhao, K Hoti, H Wang, A Raghu, D Katabi Nature medicine 27 (4), 727-735, 2021 | 44 | 2021 |
| Behaviour policy estimation in off-policy policy evaluation: Calibration matters A Raghu, O Gottesman, Y Liu, M Komorowski, A Faisal, F Doshi-Velez, ... arXiv preprint arXiv:1807.01066, 2018 | 39 | 2018 |
| Meta-learning to improve pre-training A Raghu, J Lorraine, S Kornblith, M McDermott, DK Duvenaud Advances in Neural Information Processing Systems 34, 23231-23244, 2021 | 31 | 2021 |
| Teaching with commentaries A Raghu, M Raghu, S Kornblith, D Duvenaud, G Hinton arXiv preprint arXiv:2011.03037, 2020 | 29 | 2020 |
| Detecting surface flaws using computer vision A Raghu, J Rutland, C Leistner, AP Torres US Patent 10,346,969, 2019 | 28 | 2019 |
| A deep learning model for inferring elevated pulmonary capillary wedge pressures from the 12-lead electrocardiogram DE Schlesinger, N Diamant, A Raghu, E Reinertsen, K Young, P Batra, ... JACC: Advances 1 (1), 100003, 2022 | 15 | 2022 |
| Data augmentation for electrocardiograms A Raghu, D Shanmugam, E Pomerantsev, J Guttag, CM Stultz Conference on Health, Inference, and Learning, 282-310, 2022 | 14 | 2022 |
| Learning to predict with supporting evidence: Applications to clinical risk prediction A Raghu, J Guttag, K Young, E Pomerantsev, AV Dalca, CM Stultz Proceedings of the Conference on Health, Inference, and Learning, 95-104, 2021 | 8 | 2021 |
| Contrastive Pre-Training for Multimodal Medical Time Series A Raghu, P Chandak, R Alam, J Guttag, C Stultz NeurIPS 2022 Workshop on Learning from Time Series for Health, 2022 | 7 | 2022 |
| ECG-guided non-invasive estimation of pulmonary congestion in patients with heart failure A Raghu, D Schlesinger, E Pomerantsev, S Devireddy, P Shah, J Garasic, ... Scientific Reports 13 (1), 3923, 2023 | 6 | 2023 |
| Reinforcement learning for sepsis treatment: Baselines and analysis A Raghu | 4 | 2019 |
| Sequential multi-dimensional self-supervised learning for clinical time series A Raghu, P Chandak, R Alam, J Guttag, C Stultz International Conference on Machine Learning, 28531-28548, 2023 | 2 | 2023 |
| Data-Efficient Machine Learning with Applications to Cardiology A Raghu Massachusetts Institute of Technology, 2024 | | 2024 |
