| Web-scale k-means clustering D Sculley Proceedings of the 19th international conference on World wide web, 1177-1178, 2010 | 1147 | 2010 |
| 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, J Dillon, ... Advances in neural information processing systems 32, 2019 | 1082 | 2019 |
| 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, 2015 | 1056 | 2015 |
| 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 | 991 | 2013 |
| 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 | 681 | 2017 |
| Underspecification presents challenges for credibility in modern machine learning A D’Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... Journal of Machine Learning Research, 2020 | 432 | 2020 |
| Relaxed online SVMs for spam filtering D Sculley, GM Wachman Proceedings of the 30th annual international ACM SIGIR conference on …, 2007 | 345 | 2007 |
| Machine learning: The high interest credit card of technical debt D Sculley, G Holt, D Golovin, E Davydov, T Phillips, D Ebner, ... | 309 | 2014 |
| Combined regression and ranking D Sculley Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010 | 188 | 2010 |
| 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 | 180 | 2017 |
| The ML test score: A rubric for ML production readiness and technical debt reduction E Breck, S Cai, E Nielsen, M Salib, D Sculley 2017 IEEE International Conference on Big Data (Big Data), 1123-1132, 2017 | 175 | 2017 |
| Tensorflow. js: Machine learning for the web and beyond D Smilkov, N Thorat, Y Assogba, C Nicholson, N Kreeger, P Yu, S Cai, ... Proceedings of Machine Learning and Systems 1, 309-321, 2019 | 157 | 2019 |
| Winner's curse? On pace, progress, and empirical rigor D Sculley, J Snoek, A Wiltschko, A Rahimi | 154 | 2018 |
| 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 | 145 | 2020 |
| Online active learning methods for fast label-efficient spam filtering. D Sculley CEAS 7, 143, 2007 | 140 | 2007 |
| Large scale learning to rank D Sculley | 139 | 2009 |
| Direct-manipulation visualization of deep networks D Smilkov, S Carter, D Sculley, FB Viégas, M Wattenberg arXiv preprint arXiv:1708.03788, 2017 | 136 | 2017 |
| 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 | 135 | 2009 |
| Rank aggregation for similar items D Sculley Proceedings of the 2007 SIAM international conference on data mining, 587-592, 2007 | 134 | 2007 |
| Compression and machine learning: A new perspective on feature space vectors D Sculley, CE Brodley Data Compression Conference (DCC'06), 332-341, 2006 | 127 | 2006 |
Daniel GolovinGoogleVerified email at google.com
