Robert C. Williamson

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Alex SmolaBoson AI確認したメールアドレス: smola.org
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETH確認したメールアドレス: tuebingen.mpg.de
Peter BartlettProfessor, EECS and Statistics, UC Berkeley確認したメールアドレス: cs.berkeley.edu
John Shawe-TaylorUCL確認したメールアドレス: cs.ucl.ac.uk
Rodney A. KennedyEmeritus Professor, Australian National University確認したメールアドレス: anu.edu.au
Mark D. ReidApple確認したメールアドレス: reid.name
John C. PlattGoogle確認したメールアドレス: google.com
Aditya Krishna MenonResearch Scientist, Google確認したメールアドレス: google.com
Ralf HerbrichHasso Plattner Institute確認したメールアドレス: hpi.de
Cheng Soon OngData61, CSIRO, Canberra確認したメールアドレス: anu.edu.au
Jyrki KivinenProfessor of Computer Science, University of Helsinki確認したメールアドレス: cs.helsinki.fi
Eric A. LehmannCSIRO Data61確認したメールアドレス: csiro.au
Iven MareelsFederation University Australia確認したメールアドレス: federation.edu.au
Wee Sun LeeProfessor, Department of Computer Science, National University of Singapore確認したメールアドレス: comp.nus.edu.sg
Thushara AbhayapalaProfessor, Research School of Engineering, Australian National University確認したメールアドレス: anu.edu.au
Brian AndersonAustralian National University確認したメールアドレス: anu.edu.au
Peter J. KootsookosCT State Community College, Middlesex確認したメールアドレス: ieee.org
Richard NockGoogle Research確認したメールアドレス: uniagro.fr
Brian C. LovellProfessor, The University of Queensland確認したメールアドレス: itee.uq.edu.au
William SetharesProfessor of Electrical Engineering, University of Wisconsin確認したメールアドレス: wisc.edu

フォロー

Robert C. Williamson

University of Tübingen

確認したメールアドレス: uni-tuebingen.de - ホームページ

machine learning learning theory imprecise probability risk measures


タイトル引用回数順公開年順タイトル順	引用先引用先	年
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	7535	2001
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3598	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	3180	1999
A Generalized Representer Theorem B Scholkopf, R Herbrich, A Smola, R Williamson	2353	2000
Online learning with kernels J Kivinen, AJ Smola, RC Williamson IEEE transactions on signal processing 52 (8), 2165-2176, 2004	1390	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	752	1998
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	545	1990
The cost of fairness in binary classification AK Menon, RC Williamson Conference on Fairness, accountability and transparency, 107-118, 2018	514	2018
Clustering: Science or art? U von Luxburg, R Williamson, I Guyon Journal of Machine Learning Research 27, 65-80, 2012	512*	2012
Learning the kernel with hyperkernels CS Ong, A Smola, B Williamson Journal of Machine Learning Research 6, 1045-1071, 2005	473	2005
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	446	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	396	1995
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	367	2015
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	308	1997
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	283	2007
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	277	2015
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	264	1998
Composite binary losses MD Reid, RC Williamson The Journal of Machine Learning Research 11, 2387-2422, 2010	261	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	231	2001
Information, Divergence and Risk for Binary Experiments. MD Reid, RC Williamson Journal of Machine Learning Research 12 (3), 2011	229	2011

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