| Chemical gas sensor drift compensation using classifier ensembles A Vergara, S Vembu, T Ayhan, MA Ryan, ML Homer, R Huerta Sensors and Actuators B: Chemical 166, 320-329, 2012 | 725 | 2012 |
| PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors AG Deshwar, S Vembu, CK Yung, GH Jang, L Stein, Q Morris Genome biology 16, 1-20, 2015 | 456 | 2015 |
| Inferring clonal evolution of tumors from single nucleotide somatic mutations W Jiao, S Vembu, AG Deshwar, L Stein, Q Morris BMC bioinformatics 15, 1-16, 2014 | 277 | 2014 |
| DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO) D Oberle, A Ankolekar, P Hitzler, P Cimiano, M Sintek, M Kiesel, ... Journal of Web Semantics 5 (3), 156-174, 2007 | 207* | 2007 |
| Label ranking algorithms: A survey S Vembu, T Gärtner Preference learning, 45-64, 2010 | 188 | 2010 |
| Separation of vocals from polyphonic audio recordings S Vembu, S Baumann Proceedings of the 6th International Conference of Music Information Retrieval, 2005 | 161 | 2005 |
| Beam search algorithms for multilabel learning A Kumar, S Vembu, AK Menon, C Elkan Machine learning 92, 65-89, 2013 | 153* | 2013 |
| Predicting accurate probabilities with a ranking loss A Menon, X Jiang, S Vembu, C Elkan, L Ohno-Machado Proceedings of the 29th International Conference on Machine Learning, 2012 | 82 | 2012 |
| Using the electronic medical record to identify patients at high risk for frequent emergency department visits and high system costs DW Frost, S Vembu, J Wang, K Tu, Q Morris, HB Abrams The American journal of medicine 130 (5), 601. e17-601. e22, 2017 | 52 | 2017 |
| Towards bridging the semantic gap in multimedia annotation and retrieval S Vembu, M Kiesel, M Sintek, S Baumann Proceedings of the 1st International Workshop on Semantic Web Annotations …, 2006 | 51 | 2006 |
| RNAcompete-S: Combined RNA sequence/structure preferences for RNA binding proteins derived from a single-step in vitro selection KB Cook, S Vembu, KCH Ha, H Zheng, KU Laverty, TR Hughes, D Ray, ... Methods 126, 18-28, 2017 | 44 | 2017 |
| Inhibition in multiclass classification R Huerta, S Vembu, JM Amigó, T Nowotny, C Elkan Neural computation 24 (9), 2473-2507, 2012 | 38 | 2012 |
| A self-organizing map based knowledge discovery for music recommendation systems S Vembu, S Baumann Computer music modeling and retrieval, Lecture Notes in Computer Science …, 2005 | 38 | 2005 |
| On time series features and kernels for machine olfaction S Vembu, A Vergara, MK Muezzinoglu, R Huerta Sensors and Actuators B: Chemical 174, 535-546, 2012 | 35 | 2012 |
| Towards a socio-cultural compatibility of MIR systems S Baumann, T Pohle, V Shankar Proceedings of the 5th International Conference of Music Information Retrieval, 2004 | 33 | 2004 |
| Using error decay prediction to overcome practical issues of deep active learning for named entity recognition HS Chang, S Vembu, S Mohan, R Uppaal, A McCallum Machine Learning 109, 1749-1778, 2020 | 25 | 2020 |
| On structured output training: Hard cases and an efficient alternative T Gärtner, S Vembu Machine learning 76 (2-3), 227-242, 2009 | 25 | 2009 |
| Probabilistic structured predictors S Vembu, T Gärtner, M Boley Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2009 | 14 | 2009 |
| Dynamical SVM for time series classification R Huerta, S Vembu, MK Muezzinoglu, A Vergara Proceedings of the Joint 34th DAGM and 36th OAGM Symposium, 2012 | 13 | 2012 |
| Interactive learning from multiple noisy labels S Vembu, S Zilles Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016 | 10 | 2016 |
Quaid MorrisComputational and Systems Biology Program, Memorial Sloan Kettering Cancer CenterVerified email at mskcc.org
