| Robust kernel density estimation JS Kim, CD Scott The Journal of Machine Learning Research 13 (1), 2529-2565, 2012 | 397 | 2012 |
| Generalizing from several related classification tasks to a new unlabeled sample G Blanchard, G Lee, C Scott Advances in neural information processing systems 24, 2011 | 381 | 2011 |
| Semi-supervised novelty detection G Blanchard, G Lee, C Scott The Journal of Machine Learning Research 11, 2973-3009, 2010 | 299 | 2010 |
| Classification with asymmetric label noise: Consistency and maximal denoising C Scott, G Blanchard, G Handy Conference on learning theory, 489-511, 2013 | 213 | 2013 |
| A Neyman-Pearson approach to statistical learning C Scott, R Nowak IEEE Transactions on Information Theory 51 (11), 3806-3819, 2005 | 198 | 2005 |
| Domain generalization by marginal transfer learning G Blanchard, AA Deshmukh, Ü Dogan, G Lee, C Scott The Journal of Machine Learning Research 22 (1), 46-100, 2021 | 189 | 2021 |
| Mixture proportion estimation via kernel embeddings of distributions H Ramaswamy, C Scott, A Tewari International conference on machine learning, 2052-2060, 2016 | 185 | 2016 |
| Learning minimum volume sets C Scott, R Nowak Advances in neural information processing systems 18, 2005 | 177 | 2005 |
| EM algorithms for multivariate Gaussian mixture models with truncated and censored data G Lee, C Scott Computational Statistics & Data Analysis 56 (9), 2816-2829, 2012 | 164 | 2012 |
| A rate of convergence for mixture proportion estimation, with application to learning from noisy labels C Scott Artificial Intelligence and Statistics, 838-846, 2015 | 161 | 2015 |
| Minimax-optimal classification with dyadic decision trees C Scott, RD Nowak IEEE transactions on information theory 52 (4), 1335-1353, 2006 | 149 | 2006 |
| Mean values of Dedekind sums JB Conrey, E Fransen, R Klein, C Scott arXiv preprint math/9410212, 1994 | 129 | 1994 |
| Robust contour matching via the order-preserving assignment problem C Scott, R Nowak IEEE Transactions on Image Processing 15 (7), 1831-1838, 2006 | 120 | 2006 |
| Performance measures for Neyman–Pearson classification C Scott IEEE Transactions on Information Theory 53 (8), 2852-2863, 2007 | 101 | 2007 |
| Distributed spatial anomaly detection P Chhabra, C Scott, ED Kolaczyk, M Crovella IEEE INFOCOM 2008-The 27th Conference on Computer Communications, 1705-1713, 2008 | 96 | 2008 |
| Calibrated asymmetric surrogate losses C Scott | 94 | 2012 |
| Adaptive hausdorff estimation of density level sets A Singh, C Scott, R Nowak | 94 | 2009 |
| Tuning support vector machines for minimax and Neyman-Pearson classification MA Davenport, RG Baraniuk, CD Scott IEEE transactions on pattern analysis and machine intelligence 32 (10), 1888 …, 2010 | 83 | 2010 |
| Multi-task learning for contextual bandits AA Deshmukh, U Dogan, C Scott Advances in neural information processing systems 30, 2017 | 82 | 2017 |
| Novelty detection: Unlabeled data definitely help C Scott, G Blanchard Artificial intelligence and statistics, 464-471, 2009 | 82 | 2009 |
