Robert C. Williamson

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Alex SmolaBoson AIAdresse e-mail validée de smola.org
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHAdresse e-mail validée de tuebingen.mpg.de
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyAdresse e-mail validée de cs.berkeley.edu
John Shawe-TaylorUCLAdresse e-mail validée de cs.ucl.ac.uk
Rodney A. KennedyEmeritus Professor, Australian National UniversityAdresse e-mail validée de anu.edu.au
John C. PlattGoogleAdresse e-mail validée de google.com
Mark D. ReidAppleAdresse e-mail validée de reid.name
Aditya Krishna MenonResearch Scientist, GoogleAdresse e-mail validée de google.com
Cheng Soon OngData61, CSIRO, CanberraAdresse e-mail validée de anu.edu.au
Ralf HerbrichHasso Plattner InstituteAdresse e-mail validée de hpi.de
Jyrki KivinenProfessor of Computer Science, University of HelsinkiAdresse e-mail validée de cs.helsinki.fi
Eric A. LehmannCSIRO Data61Adresse e-mail validée de csiro.au
Iven MareelsFederation University AustraliaAdresse e-mail validée de federation.edu.au
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeAdresse e-mail validée de comp.nus.edu.sg
Thushara AbhayapalaProfessor, Research School of Engineering, Australian National UniversityAdresse e-mail validée de anu.edu.au
Brian AndersonAustralian National UniversityAdresse e-mail validée de anu.edu.au
Peter J. KootsookosCT State Community College, MiddlesexAdresse e-mail validée de ieee.org
Brian C. LovellProfessor, The University of QueenslandAdresse e-mail validée de itee.uq.edu.au
Richard NockGoogle ResearchAdresse e-mail validée de uniagro.fr
William SetharesProfessor of Electrical Engineering, University of WisconsinAdresse e-mail validée de wisc.edu

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Robert C. Williamson

University of Tübingen

Adresse e-mail validée de uni-tuebingen.de - Page d'accueil

machine learning learning theory imprecise probability risk measures


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	7702	2001
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3643	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	3316	1999
A Generalized Representer Theorem B Scholkopf, R Herbrich, A Smola, R Williamson	2409	2000
Online learning with kernels J Kivinen, AJ Smola, RC Williamson IEEE transactions on signal processing 52 (8), 2165-2176, 2004	1393	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	765	1998
The cost of fairness in binary classification AK Menon, RC Williamson Conference on Fairness, accountability and transparency, 107-118, 2018	563	2018
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
Clustering: Science or art? U von Luxburg, R Williamson, I Guyon Journal of Machine Learning Research 27, 65-80, 2012	535*	2012
Learning the kernel with hyperkernels CS Ong, A Smola, B Williamson Journal of Machine Learning Research 6, 1045-1071, 2005	477	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	450	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	395	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	383	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	314	1997
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	289	2015
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	284	2007
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	266	1998
Composite binary losses MD Reid, RC Williamson The Journal of Machine Learning Research 11, 2387-2422, 2010	265	2010
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	236	1996
Information, Divergence and Risk for Binary Experiments. MD Reid, RC Williamson Journal of Machine Learning Research 12 (3), 2011	234	2011

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