Robert C. Williamson

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Alex SmolaBoson AIVerified email at smola.org
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified 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, Australian National UniversityVerified email at anu.edu.au
Mark D. ReidAppleVerified email at reid.name
John C. PlattGoogleVerified email at google.com
Aditya Krishna MenonResearch Scientist, GoogleVerified email at google.com
Ralf HerbrichHasso Plattner InstituteVerified email at hpi.de
Cheng Soon OngData61, CSIRO, CanberraVerified email at anu.edu.au
Jyrki KivinenProfessor of Computer Science, University of HelsinkiVerified email at cs.helsinki.fi
Eric A. LehmannCSIRO Data61Verified email at csiro.au
Iven MareelsFederation University AustraliaVerified email at federation.edu.au
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeVerified email at comp.nus.edu.sg
Thushara AbhayapalaProfessor, Research School of Engineering, Australian National UniversityVerified email at anu.edu.au
Brian AndersonAustralian National UniversityVerified email at anu.edu.au
Peter J. KootsookosCT State Community College, MiddlesexVerified email at ieee.org
Brian C. LovellProfessor, The University of QueenslandVerified email at itee.uq.edu.au
Richard NockGoogle ResearchVerified email at uniagro.fr
William SetharesProfessor of Electrical Engineering, University of WisconsinVerified email at wisc.edu

Robert C. Williamson

University of Tübingen

Verified email at uni-tuebingen.de - Homepage

machine learning learning theory imprecise probability risk measures


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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	7432	2001
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3579	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	3107	1999
A Generalized Representer Theorem B Scholkopf, R Herbrich, A Smola, R Williamson	2319	2000
Online learning with kernels J Kivinen, AJ Smola, RC Williamson IEEE transactions on signal processing 52 (8), 2165-2176, 2004	1384	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	750	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	541	1990
Clustering: Science or art? U von Luxburg, R Williamson, I Guyon Journal of Machine Learning Research 27, 65-80, 2012	502*	2012
The cost of fairness in binary classification AK Menon, RC Williamson Conference on Fairness, accountability and transparency, 107-118, 2018	496	2018
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	444	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	358	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	302	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	281	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	274	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	262	1998
Composite binary losses MD Reid, RC Williamson The Journal of Machine Learning Research 11, 2387-2422, 2010	254	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	230	2001
Information, Divergence and Risk for Binary Experiments. MD Reid, RC Williamson Journal of Machine Learning Research 12 (3), 2011	227	2011

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