Robert C. Williamson

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Alex SmolaBoson AIEmail verificata su smola.org
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHEmail verificata su tuebingen.mpg.de
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyEmail verificata su cs.berkeley.edu
John Shawe-TaylorUCLEmail verificata su cs.ucl.ac.uk
Rodney A. KennedyEmeritus Professor, Australian National UniversityEmail verificata su anu.edu.au
Mark D. ReidAppleEmail verificata su reid.name
John C. PlattGoogleEmail verificata su google.com
Aditya Krishna MenonResearch Scientist, GoogleEmail verificata su google.com
Ralf HerbrichHasso Plattner InstituteEmail verificata su hpi.de
Cheng Soon OngData61, CSIRO, CanberraEmail verificata su anu.edu.au
Jyrki KivinenProfessor of Computer Science, University of HelsinkiEmail verificata su cs.helsinki.fi
Eric A. LehmannCSIRO Data61Email verificata su csiro.au
Iven MareelsFederation University AustraliaEmail verificata su federation.edu.au
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeEmail verificata su comp.nus.edu.sg
Thushara AbhayapalaProfessor, Research School of Engineering, Australian National UniversityEmail verificata su anu.edu.au
Brian AndersonAustralian National UniversityEmail verificata su anu.edu.au
Peter J. KootsookosCT State Community College, MiddlesexEmail verificata su ieee.org
Richard NockGoogle ResearchEmail verificata su uniagro.fr
Brian C. LovellProfessor, The University of QueenslandEmail verificata su itee.uq.edu.au
William SetharesProfessor of Electrical Engineering, University of WisconsinEmail verificata su wisc.edu

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Robert C. Williamson

University of Tübingen

Email verificata su uni-tuebingen.de - Home page

machine learning learning theory imprecise probability risk measures


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	7511	2001
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3594	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	3171	1999
A Generalized Representer Theorem B Scholkopf, R Herbrich, A Smola, R Williamson	2345	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	544	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	445	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	306	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	263	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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