Yoni Halpern
Yoni Halpern
PhD Student, New York University
Verified email at cs.nyu.edu - Homepage
Cited by
Cited by
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
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
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
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), 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
Using anchors to estimate clinical state without labeled data
Y Halpern, Y Choi, S Horng, D Sontag
AMIA Annual Symposium Proceedings 2014, 606, 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
Unsupervised learning of noisy-or bayesian networks
Y Halpern, D Sontag
arXiv preprint arXiv:1309.6834, 2013
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
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
Discovering hidden variables in noisy-or networks using quartet tests
Y Jernite, Y Halpern, D Sontag
Advances in Neural Information Processing Systems, 2355-2363, 2013
Is the thrill gone?
S Arora, B Chazelle
Communications of the ACM 48 (8), 31-33, 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
Clinical tagging with joint probabilistic models
Y Halpern, S Horng, D Sontag
arXiv preprint arXiv:1608.00686, 2016
Anchored discrete factor analysis
Y Halpern, S Horng, D Sontag
arXiv preprint arXiv:1511.03299, 2015
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
Sensors on the brain
G Mone
Communications of the ACM 60 (4), 12-14, 2017
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, ...
BioRxiv, 127092, 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
Benefits of overparameterization in single-layer latent variable generative models
RD Buhai, A Risteski, Y Halpern, D Sontag
arXiv preprint arXiv:1907.00030, 2019
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