| RETAIN: An Interpretable Predictive Model in Healthcare using Reverse Time Attention Mechanism E Choi, MT Bahadori, A Schuetz, WF Stewart, J Sun Advances in Neural Information Processing Systems, 2016 | 1601 | 2016 |
| Doctor AI: Predicting Clinical Events via Recurrent Neural Networks E Choi, MT Bahadori, A Schuetz, WF Stewart, J Sun Machine Learning in Healthcare, 2016 | 1537 | 2016 |
| GRAM: Graph-based Attention Model for Healthcare Representation Learning E Choi, MT Bahadori, L Song, WF Stewart, J Sun Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 790 | 2017 |
| Multi-layer Representation Learning for Medical Concepts E Choi, MT Bahadori, E Searles, C Coffey, J Sun Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016 | 638 | 2016 |
| Deep computational phenotyping Z Che, D Kale, W Li, MT Bahadori, Y Liu Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 336 | 2015 |
| Fast multivariate spatio-temporal analysis via low rank tensor learning MT Bahadori, QR Yu, Y Liu Advances in neural information processing systems (NIPS), 3491-3499, 2014 | 228 | 2014 |
| Improving hospital mortality prediction with medical named entities and multimodal learning M Jin, MT Bahadori, A Colak, P Bhatia, B Celikkaya, R Bhakta, ... NeurIPS Workshop on Machine Learning for Health, 2018 | 81 | 2018 |
| Spectral Capsule Networks MT Bahadori International Conference on Learning Representations (ICLR Workshop), 2018 | 69 | 2018 |
| An Examination of Practical Granger Causality Inference MT Bahadori, Y Liu Proceedings of the 2013 SIAM International Conference on Data Mining, 467-475, 2013 | 66 | 2013 |
| Granger Causality Analysis in Irregular Time Series MT Bahadori, Y Liu Proceedings of the 2012 SIAM International Conference on Data Mining, 660-671, 2012 | 63 | 2012 |
| Debiasing Concept-based Explanations with Causal Analysis MT Bahadori, DE Heckerman International Conference on Learning Representations (ICLR'21), 2021 | 53* | 2021 |
| Fblg: A simple and effective approach for temporal dependence discovery from time series data D Cheng, MT Bahadori, Y Liu Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 52 | 2014 |
| FLASH: Fast Bayesian Optimization for Data Analytic Pipelines Y Zhang, MT Bahadori, H Su, J Sun Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016 | 42 | 2016 |
| Causal Phenotype Discovery via Deep Networks DC Kale, Z Che, MT Bahadori, W Li, Y Liu, R Wetzel Proceedings of the American Medical Informatics Assocation (AMIA) 2015 …, 2015 | 41 | 2015 |
| Functional Subspace Clustering with Application to Time Series MT Bahadori, D Kale, Y Fan, Y Liu Proceedings of the 32nd International Conference on Machine Learning, 2015 | 39 | 2015 |
| Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling Y Liu, T Bahadori, H Li Proceedings of the 29th International Conference on Machine Learning (ICML …, 2012 | 37 | 2012 |
| Learning with minimum supervision: A general framework for transductive transfer learning MT Bahadori, Y Liu, D Zhang 11th IEEE International Conference on Data Mining (ICDM), 61-70, 2011 | 36 | 2011 |
| Temporal-clustering invariance in irregular healthcare time series MT Bahadori, ZC Lipton CHIL '20: Proceedings of the ACM Conference on Health, Inference, and Learning, 2019 | 33 | 2019 |
| Scalable Interpretable Multi-Response Regression via SEED Z Zheng, MT Bahadori, Y Liu, J Lv Journal of Machine Learning Research, 2019 | 33* | 2019 |
| Causal Regularization MT Bahadori, K Chalupka, E Choi, R Chen, WF Stewart, J Sun arXiv preprint arXiv:1702.02604, 2017 | 32 | 2017 |
Jimeng SunProfessor at University of Illinois Urbana-ChampaignVerified email at illinois.edu
