| A practical algorithm for topic modeling with provable guarantees S Arora, R Ge, Y Halpern, D Mimno, A Moitra, D Sontag, Y Wu, M Zhu International Conference on Machine Learning, 280-288, 2013 | 398 | 2013 |
| Learning a health knowledge graph from electronic medical records M Rotmensch, Y Halpern, A Tlimat, S Horng, D Sontag Scientific reports 7 (1), 1-11, 2017 | 156 | 2017 |
| Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning S Horng, DA Sontag, Y Halpern, Y Jernite, NI Shapiro, LA Nathanson PloS one 12 (4), e0174708, 2017 | 133 | 2017 |
| Electronic medical record phenotyping using the anchor and learn framework Y Halpern, S Horng, Y Choi, D Sontag Journal of the American Medical Informatics Association 23 (4), 731-740, 2016 | 110 | 2016 |
| No classification without representation: Assessing geodiversity issues in open data sets for the developing world S Shankar, Y Halpern, E Breck, J Atwood, J Wilson, D Sculley arXiv preprint arXiv:1711.08536, 2017 | 60 | 2017 |
| The UTIAS multi-robot cooperative localization and mapping dataset KYK Leung, Y Halpern, TD Barfoot, HHT Liu The International Journal of Robotics Research 30 (8), 969-974, 2011 | 55 | 2011 |
| Using anchors to estimate clinical state without labeled data Y Halpern, Y Choi, S Horng, D Sontag AMIA Annual Symposium Proceedings 2014, 606, 2014 | 52 | 2014 |
| A comparison of dimensionality reduction techniques for unstructured clinical text Y Halpern, S Horng, LA Nathanson, NI Shapiro, D Sontag Icml 2012 workshop on clinical data analysis 6, 2012 | 36 | 2012 |
| Unsupervised learning of noisy-or bayesian networks Y Halpern, D Sontag arXiv preprint arXiv:1309.6834, 2013 | 35 | 2013 |
| Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network JM Banda, Y Halpern, D Sontag, NH Shah AMIA Summits on Translational Science Proceedings 2017, 48, 2017 | 33 | 2017 |
| Fairness is not static: deeper understanding of long term fairness via simulation studies A D'Amour, H Srinivasan, J Atwood, P Baljekar, D Sculley, Y Halpern Proceedings of the 2020 Conference on Fairness, Accountability, and …, 2020 | 26 | 2020 |
| Discovering hidden variables in noisy-or networks using quartet tests Y Jernite, Y Halpern, D Sontag Advances in Neural Information Processing Systems 26, 2355-2363, 2013 | 26 | 2013 |
| Is the thrill gone? S Arora, B Chazelle Communications of the ACM 48 (8), 31-33, 2005 | 20 | 2005 |
| Predicting chief complaints at triage time in the emergency department Y Jernite, Y Halpern, S Horng, D Sontag NIPS 2013 Workshop on Machine Learning for Clinical Data Analysis and Healthcare, 2013 | 19 | 2013 |
| Clinical tagging with joint probabilistic models Y Halpern, S Horng, D Sontag Machine Learning for Healthcare Conference, 209-225, 2016 | 11 | 2016 |
| Learning topic models--provably and efficiently S Arora, R Ge, Y Halpern, D Mimno, A Moitra, D Sontag, Y Wu, M Zhu Communications of the ACM 61 (4), 85-93, 2018 | 9 | 2018 |
| Anchored discrete factor analysis Y Halpern, S Horng, D Sontag arXiv preprint arXiv:1511.03299, 2015 | 8 | 2015 |
| CITATIONS READS D Payne, BJL Landry Communications of the ACM 49 (11), 81, 2006 | 8 | 2006 |
| Contextual autocomplete: A novel user interface using machine learning to improve ontology usage and structured data capture for presenting problems in the emergency department NR Greenbaum, Y Jernite, Y Halpern, S Calder, LA Nathanson, D Sontag, ... BioRxiv, 127092, 2017 | 7 | 2017 |
| Contextual Prediction Models for Speech Recognition. Y Halpern, KB Hall, V Schogol, M Riley, B Roark, G Skobeltsyn, M Baeuml INTERSPEECH, 2338-2342, 2016 | 7 | 2016 |
