Robert C. Williamson

Dario García-García, Robert C. Williamson: Divergences and Risks for Multiclass Experiments. Journal of Machine Learning Research - Proceedings Track 23: 28.1-28.20 (2012)

Ulrike von Luxburg, Robert C. Williamson, Isabelle Guyon: Clustering: Science or Art? Journal of Machine Learning Research - Proceedings Track 27: 65-80 (2012)

Mark D. Reid, Robert C. Williamson, Peng Sun: The Convexity and Design of Composite Multiclass Losses. ICML 2012

Tim van Erven, Peter D. Grünwald, Mark D. Reid, Robert C. Williamson: Mixability in Statistical Learning. NIPS 2012: 1700-1708

Shie Mannor, Nathan Srebro, Robert C. Williamson (Eds.): COLT 2012 - The 25th Annual Conference on Learning Theory, June 25-27, 2012, Edinburgh, Scotland. JMLR.org 2012

Mark D. Reid, Robert C. Williamson, Peng Sun: The Convexity and Design of Composite Multiclass Losses. CoRR abs/1206.4663 (2012)

Ayman Ghoneim, Robert C. Williamson: Strategy-Proof Prediction Markets. CoRR abs/1212.5764 (2012)

Mark D. Reid, Robert C. Williamson: Information, Divergence and Risk for Binary Experiments. Journal of Machine Learning Research 12: 731-817 (2011)

Tim van Erven, Mark D. Reid, Robert C. Williamson: Mixability is Bayes Risk Curvature Relative to Log Loss. Journal of Machine Learning Research - Proceedings Track 19: 233-252 (2011)

Elodie Vernet, Robert C. Williamson, Mark D. Reid: Composite Multiclass Losses. NIPS 2011: 1224-1232

Mark D. Reid, Robert C. Williamson: Convexity of Proper Composite Binary Losses. Journal of Machine Learning Research - Proceedings Track 9: 637-644 (2010)

Mark D. Reid, Robert C. Williamson: Composite Binary Losses. Journal of Machine Learning Research 11: 2387-2422 (2010)

Mark D. Reid, Robert C. Williamson: Generalised Pinsker Inequalities. COLT 2009

Mark D. Reid, Robert C. Williamson: Surrogate regret bounds for proper losses. ICML 2009: 113

Mark D. Reid, Robert C. Williamson: Generalised Pinsker Inequalities. CoRR abs/0906.1244 (2009)

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 54(9): 4395 (2008)

Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Léon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Pascal Vincent, Jason Weston, Robert C. Williamson: The Need for Open Source Software in Machine Learning. Journal of Machine Learning Research 8: 2443-2466 (2007)

Eric A. Lehmann, Robert C. Williamson: Particle Filter Design Using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environments. EURASIP J. Adv. Sig. Proc. 2006 (2006)

Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson: Learning the Kernel with Hyperkernels. Journal of Machine Learning Research 6: 1043-1071 (2005)

Omri Guttman, S. V. N. Vishwanathan, Robert C. Williamson: Learnability of Probabilistic Automata via Oracles. ALT 2005: 171-182

Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson: Online learning with kernels. IEEE Transactions on Signal Processing 52(8): 2165-2176 (2004)

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 11(6): 826-836 (2003)

Edward Harrington, Ralf Herbrich, Jyrki Kivinen, John C. Platt, Robert C. Williamson: Online Bayes Point Machines. PAKDD 2003: 241-252

Ralf Herbrich, Robert C. Williamson: Algorithmic Luckiness. Journal of Machine Learning Research 3: 175-212 (2002)

Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson: Covering numbers for support vector machines. IEEE Transactions on Information Theory 48(1): 239-250 (2002)

Richard K. Martin, William A. Sethares, Robert C. Williamson, C. Richard Johnson Jr.: Exploiting sparsity in adaptive filters. IEEE Transactions on Signal Processing 50(8): 1883-1894 (2002)

Jyrki Kivinen, Alex J. Smola, Robert C. Williamson: Large Margin Classification for Moving Targets. ALT 2002: 113-127

Shahar Mendelson, Robert C. Williamson: Agnostic Learning Nonconvex Function Classes. COLT 2002: 1-13

Darren B. Ward, Robert C. Williamson: Particle filter beamforming for acoustic source localization in a reverberant environment. ICASSP 2002: 1777-1780

Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson: Hyperkernels. NIPS 2002: 478-485

Alex J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson: Regularized Principal Manifolds. Journal of Machine Learning Research 1: 179-209 (2001)

Robert E. Mahony, Robert C. Williamson: Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms. Journal of Machine Learning Research 1: 311-355 (2001)

Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alex J. Smola, Robert C. Williamson: Estimating the Support of a High-Dimensional Distribution. Neural Computation 13(7): 1443-1471 (2001)

Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. IEEE Transactions on Information Theory 47(6): 2516-2532 (2001)

Simon I. Hill, Robert C. Williamson: Convergence of exponentiated gradient algorithms. IEEE Transactions on Signal Processing 49(6): 1208-1215 (2001)

Ralf Herbrich, Robert C. Williamson: Algorithmic Luckiness. NIPS 2001: 391-397

Adam Kowalczyk, Alex J. Smola, Robert C. Williamson: Kernel Machines and Boolean Functions. NIPS 2001: 439-446

Jyrki Kivinen, Alex J. Smola, Robert C. Williamson: Online Learning with Kernels. NIPS 2001: 785-792

Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett: New Support Vector Algorithms. Neural Computation 12(5): 1207-1245 (2000)

Biljana D. Radlovic, Robert C. Williamson, Rodney A. Kennedy: Equalization in an acoustic reverberant environment: robustness results. IEEE Transactions on Speech and Audio Processing 8(3): 311-319 (2000)

Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Entropy Numbers of Linear Function Classes. COLT 2000: 309-319

Thore Graepel, Ralf Herbrich, Robert C. Williamson: From Margin to Sparsity. NIPS 2000: 210-216

Alex J. Smola, Zoltán L. Óvári, Robert C. Williamson: Regularization with Dot-Product Kernels. NIPS 2000: 308-314

Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson: Covering Numbers for Support Vector Machines. COLT 1999: 267-277

Alex J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf: Regularized Principal Manifolds. EuroCOLT 1999: 214-229

Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Entropy Numbers, Operators and Support Vector Kernels. EuroCOLT 1999: 285-299

Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson: The Entropy Regularization Information Criterion. NIPS 1999: 342-348

Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt: Support Vector Method for Novelty Detection. NIPS 1999: 582-588

Erik Weyer, Robert C. Williamson, Iven M. Y. Mareels: On the Relationship Between Behavioural and Standard Methods for System Identification. Automatica 34(6): 801-804 (1998)

John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony: Structural Risk Minimization Over Data-Dependent Hierarchies. IEEE Transactions on Information Theory 44(5): 1926-1940 (1998)

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: The Importance of Convexity in Learning with Squared Loss. IEEE Transactions on Information Theory 44(5): 1974-1980 (1998)

Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson: Shrinking the Tube: A New Support Vector Regression Algorithm. NIPS 1998: 330-336

Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels, William A. Sethares: Online learning via congregational gradient descent. MCSS 10(4): 331-363 (1997)

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: Correction to 'Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes'. Neural Computation 9(4): 765-769 (1997)

Jennifer A. Fulton, Robert R. Bitmead, Robert C. Williamson: Sampling rate versus quantisation in speech coders. Signal Processing 56(3): 209-218 (1997)

Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels: Decision region approximation by polynomials or neural networks. IEEE Transactions on Information Theory 43(3): 903-907 (1997)

John Shawe-Taylor, Robert C. Williamson: A PAC Analysis of a Bayesian Estimator. COLT 1997: 2-9

Peter L. Bartlett, Philip M. Long, Robert C. Williamson: Fat-Shattering and the Learnability of Real-Valued Functions. J. Comput. Syst. Sci. 52(3): 434-452 (1996)

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: Efficient agnostic learning of neural networks with bounded fan-in. IEEE Transactions on Information Theory 42(6): 2118-2132 (1996)

John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony: A Framework for Structural Risk Minimisation. COLT 1996: 68-76

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: The Importance of Convexity in Learning with Squared Loss. COLT 1996: 140-146

Uwe Helmke, Robert C. Williamson: Neural networks, rational functions, and realization theory. MCSS 8(1): 27-49 (1995)

Robert C. Williamson, Uwe Helmke: Existence and uniqueness results for neural network approximations. IEEE Trans. Neural Netw. Learning Syst. 6(1): 2-13 (1995)

Ben James, Brian D. O. Anderson, Robert C. Williamson: Characterization of threshold for single tone maximum likelihood frequency estimation. IEEE Transactions on Signal Processing 43(4): 817-821 (1995)

Mehmet Karan, Brian D. O. Anderson, Robert C. Williamson: An efficient calculation of the moments of matched and mismatched hidden Markov models. IEEE Transactions on Signal Processing 43(10): 2422-2425 (1995)

Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels, William A. Sethares: Online Learning via Congregational Gradient Descent. COLT 1995: 265-272

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: On Efficient Agnostic Learning of Linear Combinations of Basis Functions. COLT 1995: 369-376

Adam Kowalczyk, Jacek Szymanski, Peter L. Bartlett, Robert C. Williamson: Examples of learning curves from a modified VC-formalism. NIPS 1995: 344-350

Ben James, Brian D. O. Anderson, Robert C. Williamson: Conditional mean and maximum likelihood approaches to multiharmonic frequency estimation. IEEE Transactions on Signal Processing 42(6): 1366-1375 (1994)

Mehmet Karan, Robert C. Williamson, Brian D. O. Anderson: Performance of the maximum likelihood constant frequency estimator for frequency tracking. IEEE Transactions on Signal Processing 42(10): 2749-2757 (1994)

Peter L. Bartlett, Philip M. Long, Robert C. Williamson: Fat-Shattering and the Learnability of Real-Valued Functions. COLT 1994: 299-310

Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes. COLT 1994: 362-367

Brian C. Lovell, Robert C. Williamson, Boualem Boashash: The relationship between instantaneous frequency and time-frequency representations. IEEE Transactions on Signal Processing 41(3): 1458-1461 (1993)

Brian C. Lovell, Robert C. Williamson: The statistical performance of some instantaneous frequency estimators. IEEE Transactions on Signal Processing 40(7): 1708-1723 (1992)

Uwe Helmke, Robert C. Williamson: Rational Parametrizations of Neural Networks. NIPS 1992: 623-630

Robert C. Williamson: An extreme limit theorem for dependency bounds of normalized sums of random variables. Inf. Sci. 56(1-3): 113-141 (1991)

Peter L. Bartlett, Robert C. Williamson: Investigating the Distribution Assumptions in the Pac Learning Model. COLT 1991: 24-32

Robert C. Williamson, Peter L. Bartlett: Splines, Rational Functions and Neural Networks. NIPS 1991: 1040-1047

Robert C. Williamson, Tom Downs: Probabilistic arithmetic. I. Numerical methods for calculating convolutions and dependency bounds. Int. J. Approx. Reasoning 4(2): 89-158 (1990)

Robert C. Williamson: epsilon-Entropy and the Complexity of Feedforward Neural Networks. NIPS 1990: 946-952