Robert C. Williamson

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引用	27801	11352
h 指数	55	33
i10 指数	127	66

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Alex SmolaBoson AI在 smola.org 的电子邮件经过验证
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETH在 tuebingen.mpg.de 的电子邮件经过验证
Peter BartlettProfessor, EECS and Statistics, UC Berkeley在 cs.berkeley.edu 的电子邮件经过验证
John Shawe-TaylorUCL在 cs.ucl.ac.uk 的电子邮件经过验证
Rodney A. KennedyEmeritus Professor, Australian National University在 anu.edu.au 的电子邮件经过验证
John C. PlattGoogle在 google.com 的电子邮件经过验证
Mark D. ReidApple在 reid.name 的电子邮件经过验证
Aditya Krishna MenonResearch Scientist, Google在 google.com 的电子邮件经过验证
Ralf HerbrichHasso Plattner Institute在 hpi.de 的电子邮件经过验证
Cheng Soon OngData61, CSIRO, Canberra在 anu.edu.au 的电子邮件经过验证
Jyrki KivinenProfessor of Computer Science, University of Helsinki在 cs.helsinki.fi 的电子邮件经过验证
Eric A. LehmannCSIRO Data61在 csiro.au 的电子邮件经过验证
Iven MareelsFederation University Australia在 federation.edu.au 的电子邮件经过验证
Wee Sun LeeProfessor, Department of Computer Science, National University of Singapore在 comp.nus.edu.sg 的电子邮件经过验证
Thushara AbhayapalaProfessor, Research School of Engineering, Australian National University在 anu.edu.au 的电子邮件经过验证
Peter J. KootsookosMiddlesex Community College在 ieee.org 的电子邮件经过验证
Brian AndersonAustralian National University, Hangzhou Dianzi University, Data61-CSIRO在 anu.edu.au 的电子邮件经过验证
Brian C. LovellProfessor, The University of Queensland在 itee.uq.edu.au 的电子邮件经过验证
William SetharesProfessor of Electrical Engineering, University of Wisconsin在 wisc.edu 的电子邮件经过验证
Klaus-Robert MüllerTU Berlin & Korea University & Google Deepmind & Max Planck Institute for Informatics, Germany在 tu-berlin.de 的电子邮件经过验证

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Robert C. Williamson

University of Tübingen

在 uni-tuebingen.de 的电子邮件经过验证 - 首页

machine learning learning theory imprecise probability risk measures


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Estimating the support of a high-dimensional distribution B Schölkopf, JC Platt, J Shawe-Taylor, AJ Smola, RC Williamson Neural computation 13 (7), 1443-1471, 2001	6878	2001
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3458	2000
Support vector method for novelty detection B Schölkopf, RC Williamson, A Smola, J Shawe-Taylor, J Platt Advances in neural information processing systems 12, 1999	2285	1999
A Generalized Representer Theorem B Scholkopf, R Herbrich, A Smola, R Williamson	2147	2000
Online learning with kernels J Kivinen, AJ Smola, RC Williamson IEEE transactions on signal processing 52 (8), 2165-2176, 2004	1326	2004
Structural risk minimization over data-dependent hierarchies J Shawe-Taylor, PL Bartlett, RC Williamson, M Anthony IEEE transactions on Information Theory 44 (5), 1926-1940, 1998	724	1998
Support vector method for novelty detection B Scholkopf, R Williamson, A Smola, J Shawe-Taylor, J Platt Advances in neural information processing systems 12 (3), 582-588, 2000	597	2000
Probabilistic arithmetic. I. Numerical methods for calculating convolutions and dependency bounds RC Williamson, T Downs International journal of approximate reasoning 4 (2), 89-158, 1990	529	1990
Learning the kernel with hyperkernels CS Ong, A Smola, B Williamson Journal of Machine Learning Research 6, 1045-1071, 2005	459	2005
Clustering: Science or art? U von Luxburg, R Williamson, I Guyon Journal of Machine Learning Research 27, 65-80, 2012	447*	2012
Particle filtering algorithms for tracking an acoustic source in a reverberant environment DB Ward, EA Lehmann, RC Williamson IEEE Transactions on speech and audio processing 11 (6), 826-836, 2003	432	2003
Theory and design of broadband sensor arrays with frequency invariant farfield beam patterns DB Ward, RA Kennedy, RC Williamson The Journal of the Acoustical Society of America 97 (2), 1023-1034, 1995	392	1995
The cost of fairness in binary classification AK Menon, RC Williamson Conference on Fairness, accountability and transparency, 107-118, 2018	389	2018
Learning with symmetric label noise: The importance of being unhinged B Van Rooyen, A Menon, RC Williamson Advances in neural information processing systems 28, 2015	304	2015
The need for open source software in machine learning S Sonnenburg, ML Braun, CS Ong, S Bengio, L Bottou, G Holmes, ... JMLR 8, 2443-2466, 2007	264	2007
A PAC analysis of a Bayesian estimator J Shawe-Taylor, RC Williamson Proceedings of the tenth annual conference on Computational learning theory, 2-9, 1997	262	1997
Shrinking the tube: a new support vector regression algorithm B Schölkopf, P Bartlett, A Smola, RC Williamson Advances in neural information processing systems 11, 1998	255	1998
Learning from corrupted binary labels via class-probability estimation A Menon, B Van Rooyen, CS Ong, B Williamson International conference on machine learning, 125-134, 2015	242	2015
Composite binary losses MD Reid, RC Williamson The Journal of Machine Learning Research 11, 2387-2422, 2010	224	2010
Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators RC Williamson, AJ Smola, B Scholkopf IEEE transactions on Information Theory 47 (6), 2516-2532, 2001	223	2001

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