[-] Name Disambiguation results for 2 "Robert C. Williamson": [Show all "Robert C. Williamson"]

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Computer Sciences Laboratory Research School of Information Sci ...
2 h-index:2 #papers:5
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Robert C. Williamson
(ALIAS: Robert Williamson) [FOAF]  [Follow]

Position: Scientific Director Professor
Affiliation: Computer Sciences Laboratory Research School of Information Sciences and Engineering Australian National University
Address: National ICT Australia, Locked Bag 8001, Canberra, ACT 2601, Australia
Phone: + 61 2 6125 3139
Fax: + 61 2 6125 8651
Email:
Homepage: http://axiom.anu.edu.au/~williams/
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Statistics: H-index: 20 (See all experts' h-index.)
total citation number: 3699
highest-cited paper: Estimating the Support of a High-Dimensional Distribution (2001) at Neural Computation (Cited By 1076)

Research Interest:

Online Learning, Squared Loss, New Support, Real-Valued Functions, Agnostic Learning Nonconvex Function

Show Temporal Interests (Do you want to see the change of his/her research interests?)


Publications: [Edit disambiguation Result]

2009(2)
[44]Mark D. Reid, Robert C. Williamson. Surrogate regret bounds for proper losses.  ICML'2009. pp.113~113    Cited By 3[Bibtex]
[43]Mark D. Reid, Robert C. Williamson. Generalised Pinsker Inequalities. CoRR, 2009.     Cited By 1[Bibtex]
2008(1)
[42]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson. Correction to 'The Importance of Convexity in Learning With Squared Loss. IEEE Transactions on Information Theory, 2008: 4395~4395    Cited By 65[Bibtex]
2005(1)
[41]Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson. Learning the Kernel with Hyperkernels. Journal of Machine Learning Research, 2005: 1043~1071    Cited By 139[Bibtex]
2003(2)
[40]Edward Harrington, Ralf Herbrich, Jyrki Kivinen, John C. Platt, Robert C. Williamson. Online Bayes Point Machines.  PAKDD'2003. pp.241~252    Cited By 18[Bibtex] [PDF]
[39]Darren B. Ward, Eric A. Lehmann, Robert C. Williamson. Particle filtering algorithms for tracking an acoustic source in a reverberant environment. IEEE Transactions on Speech and Audio Processing, 2003: 826~836    Cited By 136[Bibtex] [PDF]
2002(5)
[38]Jyrki Kivinen, Alex J. Smola, Robert C. Williamson. Large Margin Classification for Moving Targets.  ALT'2002. pp.113~127    Cited By 7[Bibtex] [PDF]
[37]Shahar Mendelson, Robert C. Williamson. Agnostic Learning Nonconvex Function Classes.  COLT'2002. pp.1~13    Cited By 2[Bibtex]
[36]Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson. Hyperkernels.  NIPS'2002. pp.478~485    Cited By 50[Bibtex] [PDF]
[35]Ralf Herbrich, Robert C. Williamson. Algorithmic Luckiness. Journal of Machine Learning Research, 2002: 175~212    Cited By 37[Bibtex] [PDF]
[34]Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson. Covering numbers for support vector machines. IEEE Transactions on Information Theory, 2002: 239~250    Cited By 41[Bibtex]
2001(7)
[33]Jyrki Kivinen, Alex J. Smola, Robert C. Williamson. Online Learning with Kernels.  NIPS'2001. pp.785~792    Cited By 224[Bibtex] [PDF]
[32]Ralf Herbrich, Robert C. Williamson. Algorithmic Luckiness.  NIPS'2001. pp.391~397    Cited By 37[Bibtex] [PDF]
[31]Adam Kowalczyk, Alex J. Smola, Robert C. Williamson. Kernel Machines and Boolean Functions.  NIPS'2001. pp.439~446    Cited By 9[Bibtex] [PDF]
[30]Robert E. Mahony, Robert C. Williamson. Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms. Journal of Machine Learning Research, 2001: 311~355    Cited By 14[Bibtex]
[29]Alex J. Smola, Sebastian Mika, Bernhard Scholkopf, Robert C. Williamson. Regularized Principal Manifolds. Journal of Machine Learning Research, 2001: 179~209    Cited By 71[Bibtex] [PDF]
[28]Bernhard Scholkopf, John C. Platt, John Shawe-Taylor, Alex J. Smola, Robert C. Williamson. Estimating the Support of a High-Dimensional Distribution. Neural Computation, 2001: 1443~1471    Cited By 1076[Bibtex]
[27]Robert C. Williamson, Alex J. Smola, Bernhard Scholkopf. Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. IEEE Transactions on Information Theory, 2001: 2516~2532    Cited By 123[Bibtex] [PDF]
2000(4)
[26]Robert C. Williamson, Alex J. Smola, Bernhard Scholkopf. Entropy Numbers of Linear Function Classes.  COLT'2000. pp.309~319    Cited By 14[Bibtex] [PDF]
[25]Thore Graepel, Ralf Herbrich, Robert C. Williamson. From Margin to Sparsity.  NIPS'2000. pp.210~216    Cited By 44[Bibtex] [PDF]
[24]Alex J. Smola, Zoltan L. Ovari, Robert C. Williamson. Regularization with Dot-Product Kernels.  NIPS'2000. pp.308~314    Cited By 21[Bibtex]
[23]Bernhard Scholkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett. New Support Vector Algorithms. Neural Computation, 2000: 1207~1245    Cited By 858[Bibtex]
1999(5)
[22]Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson. Covering Numbers for Support Vector Machines.  COLT'1999. pp.267~277    Cited By 41[Bibtex]
[21]Alex J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Scholkopf. Regularized Principal Manifolds.  EuroCOLT'1999. pp.214~229   [Bibtex] [PDF]
[20]Robert C. Williamson, Alex J. Smola, Bernhard Scholkopf. Entropy Numbers, Operators and Support Vector Kernels.  EuroCOLT'1999. pp.285~299    Cited By 22[Bibtex] [PDF]
[19]Bernhard Scholkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt. Support Vector Method for Novelty Detection.  NIPS'1999. pp.582~588    Cited By 197[Bibtex] [PDF]
[18]Alex J. Smola, John Shawe-Taylor, Bernhard Scholkopf, Robert C. Williamson. The Entropy Regularization Information Criterion.  NIPS'1999. pp.342~348   [Bibtex] [PDF]
1998(3)
[17]Bernhard Scholkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson. Shrinking the Tube: A New Support Vector Regression Algorithm.  NIPS'1998. pp.330~336   [Bibtex] [PDF]
[16]John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony. Structural Risk Minimization Over Data-Dependent Hierarchies. IEEE Transactions on Information Theory, 1998: 1926~1940    Cited By 362[Bibtex] [PDF]
[15]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson. The Importance of Convexity in Learning with Squared Loss. IEEE Transactions on Information Theory, 1998: 1974~1980    Cited By 65[Bibtex] [PDF]
1997(3)
[14]John Shawe-Taylor, Robert C. Williamson. A PAC Analysis of a Bayesian Estimator.  COLT'1997. pp.2~9    Cited By 24[Bibtex]
[13]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson. Correction to 'Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes. Neural Computation, 1997: 765~769   [Bibtex]
[12]Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels. Decision region approximation by polynomials or neural networks. IEEE Transactions on Information Theory, 1997: 903~907    Cited By 7[Bibtex]
1996(4)
[11]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson. The Importance of Convexity in Learning with Squared Loss.  COLT'1996. pp.140~146    Cited By 65[Bibtex] [PDF]
[10]John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony. A Framework for Structural Risk Minimisation.  COLT'1996. pp.68~76    Cited By 69[Bibtex] [PDF]
[9]Peter L. Bartlett, Philip M. Long, Robert C. Williamson. Fat-Shattering and the Learnability of Real-Valued Functions. J. Comput. Syst. Sci., 1996: 434~452    Cited By 98[Bibtex] [PDF]
[8]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson. Efficient agnostic learning of neural networks with bounded fan-in. IEEE Transactions on Information Theory, 1996: 2118~2132    Cited By 123[Bibtex] [PDF]
1995(3)
[7]Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels, William A. Sethares. Online Learning via Congregational Gradient Descent.  COLT'1995. pp.265~272    Cited By 5[Bibtex]
[6]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson. On Efficient Agnostic Learning of Linear Combinations of Basis Functions.  COLT'1995. pp.369~376    Cited By 19[Bibtex] [PDF]
[5]Adam Kowalczyk, Jacek Szymanski, Peter L. Bartlett, Robert C. Williamson. Examples of learning curves from a modified VC-formalism.  NIPS'1995. pp.344~350    Cited By 1[Bibtex] [PDF]
1994(2)
[4]Peter L. Bartlett, Philip M. Long, Robert C. Williamson. Fat-Shattering and the Learnability of Real-Valued Functions.  COLT'1994. pp.299~310    Cited By 98[Bibtex] [PDF]
[3]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson. Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes.  COLT'1994. pp.362~367   [Bibtex] [PDF]
1991(2)
[2]Peter L. Bartlett, Robert C. Williamson. Investigating the Distribution Assumptions in the Pac Learning Model.  COLT'1991. pp.24~32    Cited By 16[Bibtex]
[1]Robert C. Williamson, Peter L. Bartlett. Splines, Rational Functions and Neural Networks.  NIPS'1991. pp.1040~1047    Cited By 1[Bibtex]