| Web-scale k-means clustering D Sculley Proceedings of the 19th international conference on World wide web, 1177-1178, 2010 | 711 | 2010 |
| Ad click prediction: a view from the trenches HB McMahan, G Holt, D Sculley, M Young, D Ebner, J Grady, L Nie, ... Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 686 | 2013 |
| Hidden technical debt in machine learning systems D Sculley, G Holt, D Golovin, E Davydov, T Phillips, D Ebner, ... Advances in neural information processing systems 28, 2503-2511, 2015 | 420 | 2015 |
| Google vizier: A service for black-box optimization D Golovin, B Solnik, S Moitra, G Kochanski, J Karro, D Sculley Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 355 | 2017 |
| Relaxed online SVMs for spam filtering D Sculley, GM Wachman Proceedings of the 30th annual international ACM SIGIR conference on …, 2007 | 315 | 2007 |
| Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, JV Dillon, ... arXiv preprint arXiv:1906.02530, 2019 | 241 | 2019 |
| Machine learning: The high interest credit card of technical debt D Sculley, G Holt, D Golovin, E Davydov, T Phillips, D Ebner, ... | 191 | 2014 |
| Combined regression and ranking D Sculley Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010 | 153 | 2010 |
| Large scale learning to rank D Sculley | 128 | 2009 |
| Online active learning methods for fast label-efficient spam filtering. D Sculley CEAS 7, 143, 2007 | 121 | 2007 |
| Predicting bounce rates in sponsored search advertisements D Sculley, RG Malkin, S Basu, RJ Bayardo Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009 | 117 | 2009 |
| Compression and machine learning: A new perspective on feature space vectors D Sculley, CE Brodley Data Compression Conference (DCC'06), 332-341, 2006 | 116 | 2006 |
| Rank aggregation for similar items D Sculley Proceedings of the 2007 SIAM international conference on data mining, 587-592, 2007 | 113 | 2007 |
| Winner's curse? On pace, progress, and empirical rigor D Sculley, J Snoek, A Wiltschko, A Rahimi | 87 | 2018 |
| Detecting adversarial advertisements in the wild D Sculley, ME Otey, M Pohl, B Spitznagel, J Hainsworth, Y Zhou Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011 | 86 | 2011 |
| Direct-manipulation visualization of deep networks D Smilkov, S Carter, D Sculley, FB Viégas, M Wattenberg arXiv preprint arXiv:1708.03788, 2017 | 83 | 2017 |
| Meaning and mining: the impact of implicit assumptions in data mining for the humanities D Sculley, BM Pasanek Literary and Linguistic Computing 23 (4), 409-424, 2008 | 83 | 2008 |
| Systems and methods for identifying unwanted or harmful electronic text D Sculley, G Wachman, CE Brokley US Patent App. 12/376,970, 2010 | 79 | 2010 |
| Tensorflow. js: Machine learning for the web and beyond D Smilkov, N Thorat, Y Assogba, A Yuan, N Kreeger, P Yu, K Zhang, ... arXiv preprint arXiv:1901.05350, 2019 | 59 | 2019 |
| 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 | 57 | 2017 |
Daniel GolovinGoogleEmail verificata su google.com
