| 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 | 1768 | 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 | 1528 | 2015 |
| Web-scale k-means clustering D Sculley Proceedings of the 19th international conference on World wide web, 1177-1178, 2010 | 1480 | 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 | 1133 | 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 | 863 | 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 23 (226), 1-61, 2022 | 740 | 2022 |
| Machine learning: The high interest credit card of technical debt D Sculley, G Holt, D Golovin, E Davydov, T Phillips, D Ebner, ... SE4ML: software engineering for machine learning (NIPS 2014 Workshop) 111, 112, 2014 | 374 | 2014 |
| Relaxed online SVMs for spam filtering D Sculley, GM Wachman Proceedings of the 30th annual international ACM SIGIR conference on …, 2007 | 352 | 2007 |
| 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 | 305 | 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 | 266 | 2017 |
| Using deep learning to annotate the protein universe ML Bileschi, D Belanger, DH Bryant, T Sanderson, B Carter, D Sculley, ... Nature Biotechnology 40 (6), 932-937, 2022 | 259 | 2022 |
| 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 | 255 | 2020 |
| 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 | 222 | 2019 |
| Evaluating prediction-time batch normalization for robustness under covariate shift Z Nado, S Padhy, D Sculley, A D'Amour, B Lakshminarayanan, J Snoek arXiv preprint arXiv:2006.10963, 2020 | 214 | 2020 |
| Combined regression and ranking D Sculley Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010 | 204 | 2010 |
| Winner's curse? On pace, progress, and empirical rigor D Sculley, J Snoek, A Wiltschko, A Rahimi | 183 | 2018 |
| Direct-manipulation visualization of deep networks D Smilkov, S Carter, D Sculley, FB Viégas, M Wattenberg arXiv preprint arXiv:1708.03788, 2017 | 165 | 2017 |
| Large scale learning to rank D Sculley | 151 | 2009 |
| Online active learning methods for fast label-efficient spam filtering. D Sculley CEAS 7, 143, 2007 | 147 | 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 | 142 | 2009 |
Daniel GolovinGoogle在 google.com 的电子邮件经过验证
