Robert C. Williamson - Google Scholar Citations

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Alex SmolaAmazon Web ServicesVerified email at smola.org
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; and Distinguished Amazon ScholarVerified email at tuebingen.mpg.de
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
John Shawe-TaylorUCLVerified email at cs.ucl.ac.uk
Rodney A. KennedyEmeritus Professor, School of Engineering, Australian National UniversityVerified email at anu.edu.au
Mark D. ReidResearch Fellow, The Australian National University and NICTAVerified email at anu.edu.au
John C. PlattGoogleVerified email at google.com
Ralf HerbrichAmazonVerified email at amazon.com
Cheng Soon OngData61, CSIRO, CanberraVerified email at anu.edu.au
Jyrki KivinenProfessor of Computer Science, University of HelsinkiVerified email at cs.helsinki.fi
Aditya Krishna MenonResearch Scientist, GoogleVerified email at google.com
Eric A. LehmannCSIRO Data61Verified email at csiro.au
Iven MareelsIBM Research Australia & Professor at The University of MelbourneVerified email at au1.ibm.com
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeVerified email at comp.nus.edu.sg
Peter J. KootsookosSelfVerified email at ieee.org
Thushara AbhayapalaProfessor, Research School of Engineering, Australian National UniversityVerified email at anu.edu.au
Brian AndersonAustralian National University, Hangzhou Dianzi University, Data61-CSIROVerified email at anu.edu.au
Brian C. LovellProfessor, The University of QueenslandVerified email at itee.uq.edu.au
William SetharesProfessor of Electrical Engineering, University of WisconsinVerified email at wisc.edu
Thore GraepelResearch Scientist, Google DeepMind, and Professor of Computer Science, UCLVerified email at ucl.ac.uk

Robert C. Williamson

Professor of Computer Science, Australian National University; Distinguished Researcher, DATA61

Verified email at anu.edu.au - Homepage

machine learning learning theory signal processing statistics


Title	Cited by	Year
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	4438	2001
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	2883	2000
A Generalized Representer Theorem B Scholkopf, R Herbrich, A Smola, R Williamson	1308	2000
Online learning with kernels J Kivinen, AJ Smola, RC Williamson IEEE transactions on signal processing 52 (8), 2165-2176, 2004	1019	2004
Support vector method for novelty detection B Schölkopf, RC Williamson, AJ Smola, J Shawe-Taylor, JC Platt Advances in neural information processing systems, 582-588, 2000	876	2000
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	623	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	452	1990
Learning the kernel with hyperkernels CS Ong, A Smola, B Williamson Journal of Machine Learning Research 6, 1045-1071, 2005	379	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	376	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	326	1995
Clustering: Science or art? U von Luxburg, R Williamson, I Guyon Journal of Machine Learning Research 27, 65-80, 2012	252*	2012
Shrinking the tube: a new support vector regression algorithm B Schölkopf, PL Bartlett, AJ Smola, RC Williamson Advances in neural information processing systems, 330-336, 1999	207	1999
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	189	2001
The need for open source software in machine learning SÃ Sonnenburg, ML Braun, CS Ong, S Bengio, L Bottou, G Holmes, ... Journal of Machine Learning Research 8 (Oct), 2443-2466, 2007	184	2007
Efficient agnostic learning of neural networks with bounded fan-in WS Lee, PL Bartlett, RC Williamson IEEE Transactions on Information Theory 42 (6), 2118-2132, 1996	179	1996
Support vector regression with automatic accuracy control B Schölkopf, P Bartlett, A Smola, R Williamson International Conference on Artificial Neural Networks, 111-116, 1998	165	1998
Fat shattering and the learnability of real-valued functions PL Bartlett, PM Long, RC Williamson Journal of Computer and System Sciences 52 (3), 434-452, 1996	165	1996
The statistical performance of some instantaneous frequency estimators BC Lovell, RC Williamson IEEE Transactions on Signal Processing 40 (7), 1708-1723, 1992	159	1992
Equalization in an acoustic reverberant environment: Robustness results BD Radlovic, RC Williamson, RA Kennedy IEEE Transactions on Speech and Audio Processing 8 (3), 311-319, 2000	158	2000
Information, divergence and risk for binary experiments MD Reid, RC Williamson Journal of Machine Learning Research 12 (Mar), 731-817, 2011	143	2011

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