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 | 566 | 2013 |
Learning a health knowledge graph from electronic medical records M Rotmensch, Y Halpern, A Tlimat, S Horng, D Sontag Scientific reports 7 (1), 5994, 2017 | 494 | 2017 |
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 | 322 | 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 | 305 | 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 | 270 | 2020 |
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 | 178 | 2016 |
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 | 118 | 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 | 77 | 2014 |
Causally motivated shortcut removal using auxiliary labels M Makar, B Packer, D Moldovan, D Blalock, Y Halpern, A D’Amour International Conference on Artificial Intelligence and Statistics, 739-766, 2022 | 75 | 2022 |
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 | 66 | 2017 |
Detecting social and behavioral determinants of health with structured and free-text clinical data DJ Feller, OJB Don't Walk, J Zucker, MT Yin, P Gordon, N Elhadad Applied clinical informatics 11 (01), 172-181, 2020 | 63 | 2020 |
Unsupervised learning of noisy-or bayesian networks Y Halpern, D Sontag arXiv preprint arXiv:1309.6834, 2013 | 47 | 2013 |
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 | 46 | 2012 |
Measuring recommender system effects with simulated users S Yao, Y Halpern, N Thain, X Wang, K Lee, F Prost, EH Chi, J Chen, ... arXiv preprint arXiv:2101.04526, 2021 | 45 | 2021 |
Discovering hidden variables in noisy-or networks using quartet tests Y Jernite, Y Halpern, D Sontag Advances in Neural Information Processing Systems 26, 2013 | 40 | 2013 |
Improving documentation of presenting problems in the emergency department using a domain-specific ontology and machine learning-driven user interfaces NR Greenbaum, Y Jernite, Y Halpern, S Calder, LA Nathanson, ... International journal of medical informatics 132, 103981, 2019 | 36 | 2019 |
Empirical study of the benefits of overparameterization in learning latent variable models RD Buhai, Y Halpern, Y Kim, A Risteski, D Sontag International Conference on Machine Learning, 1211-1219, 2020 | 30 | 2020 |
No classification without representation: Assessing geodiversity issues in open data sets for the developing world. arXiv 2017 S Shankar, Y Halpern, E Breck, J Atwood, J Wilson, D Sculley arXiv preprint arXiv:1711.08536, 2017 | 30 | 2017 |
Building healthy recommendation sequences for everyone: A safe reinforcement learning approach A Singh, Y Halpern, N Thain, K Christakopoulou, E Chi, J Chen, A Beutel FAccTRec Workshop, 2020 | 26 | 2020 |
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 | 25 | 2013 |