Clayton Scott
Clayton Scott
Professor of Electrical Engineering and Computer Science, University of Michigan
Verified email at - Homepage
Cited by
Cited by
Robust kernel density estimation
JS Kim, CD Scott
The Journal of Machine Learning Research 13 (1), 2529-2565, 2012
Semi-supervised novelty detection
G Blanchard, G Lee, C Scott
The Journal of Machine Learning Research 11, 2973-3009, 2010
A Neyman-Pearson approach to statistical learning
C Scott, R Nowak
IEEE Transactions on Information Theory 51 (11), 3806-3819, 2005
Learning minimum volume sets
CD Scott, RD Nowak
Journal of Machine Learning Research 7 (Apr), 665-704, 2006
Classification with asymmetric label noise: Consistency and maximal denoising
C Scott, G Blanchard, G Handy
Conference On Learning Theory, 489-511, 2013
Minimax-optimal classification with dyadic decision trees
C Scott, RD Nowak
IEEE transactions on information theory 52 (4), 1335-1353, 2006
Mean values of Dedekind sums
JB Conrey, E Fransen, R Klein, C Scott
arXiv preprint math/9410212, 1994
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
Robust contour matching via the order-preserving assignment problem
C Scott, R Nowak
IEEE Transactions on Image Processing 15 (7), 1831-1838, 2006
Generalizing from several related classification tasks to a new unlabeled sample
G Blanchard, G Lee, C Scott
Advances in neural information processing systems, 2178-2186, 2011
Distributed spatial anomaly detection
P Chhabra, C Scott, ED Kolaczyk, M Crovella
IEEE INFOCOM 2008-The 27th Conference on Computer Communications, 1705-1713, 2008
Performance measures for Neyman–Pearson classification
C Scott
IEEE Transactions on Information Theory 53 (8), 2852-2863, 2007
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
Mixture proportion estimation via kernel embeddings of distributions
H Ramaswamy, C Scott, A Tewari
International conference on machine learning, 2052-2060, 2016
A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
C Scott
Artificial Intelligence and Statistics, 838-846, 2015
Adaptive hausdorff estimation of density level sets
A Singh, C Scott, R Nowak
The Annals of Statistics 37 (5B), 2760-2782, 2009
Controlling false alarms with support vector machines
MA Davenport, RG Baraniuk, CD Scott
2006 IEEE International Conference on Acoustics Speech and Signal Processing …, 2006
Calibrated asymmetric surrogate losses
C Scott
Electronic Journal of Statistics 6, 958-992, 2012
The value of defibrillator electrograms for recognition of clinical ventricular tachycardias and for pace mapping of post-infarction ventricular tachycardia
K Yoshida, TY Liu, C Scott, A Hero, M Yokokawa, S Gupta, E Good, ...
Journal of the American College of Cardiology 56 (12), 969-979, 2010
Novelty detection: Unlabeled data definitely help
C Scott, G Blanchard
Artificial intelligence and statistics, 464-471, 2009
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