| 2009 | ||
|---|---|---|
| 49 | Mark D. Reid, Robert C. Williamson: Surrogate regret bounds for proper losses. ICML 2009: 113 | |
| 48 | Mark D. Reid, Robert C. Williamson: Generalised Pinsker Inequalities CoRR abs/0906.1244: (2009) | |
| 2008 | ||
| 47 | 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) | |
| 2005 | ||
| 46 | Omri Guttman, S. V. N. Vishwanathan, Robert C. Williamson: Learnability of Probabilistic Automata via Oracles. ALT 2005: 171-182 | |
| 45 | Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson: Learning the Kernel with Hyperkernels. Journal of Machine Learning Research 6: 1043-1071 (2005) | |
| 2003 | ||
| 44 | Edward Harrington, Ralf Herbrich, Jyrki Kivinen, John C. Platt, Robert C. Williamson: Online Bayes Point Machines. PAKDD 2003: 241-252 | |
| 43 | 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) | |
| 2002 | ||
| 42 | Jyrki Kivinen, Alex J. Smola, Robert C. Williamson: Large Margin Classification for Moving Targets. ALT 2002: 113-127 | |
| 41 | Shahar Mendelson, Robert C. Williamson: Agnostic Learning Nonconvex Function Classes. COLT 2002: 1-13 | |
| 40 | Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson: Hyperkernels. NIPS 2002: 478-485 | |
| 39 | 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) | |
| 38 | Ralf Herbrich, Robert C. Williamson: Algorithmic Luckiness. Journal of Machine Learning Research 3: 175-212 (2002) | |
| 2001 | ||
| 37 | Ralf Herbrich, Robert C. Williamson: Algorithmic Luckiness. NIPS 2001: 391-397 | |
| 36 | Adam Kowalczyk, Alex J. Smola, Robert C. Williamson: Kernel Machines and Boolean Functions. NIPS 2001: 439-446 | |
| 35 | Jyrki Kivinen, Alex J. Smola, Robert C. Williamson: Online Learning with Kernels. NIPS 2001: 785-792 | |
| 34 | 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) | |
| 33 | Alex J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson: Regularized Principal Manifolds. Journal of Machine Learning Research 1: 179-209 (2001) | |
| 32 | 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) | |
| 31 | 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) | |
| 2000 | ||
| 30 | Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Entropy Numbers of Linear Function Classes. COLT 2000: 309-319 | |
| 29 | Thore Graepel, Ralf Herbrich, Robert C. Williamson: From Margin to Sparsity. NIPS 2000: 210-216 | |
| 28 | Alex J. Smola, Zoltán L. Óvári, Robert C. Williamson: Regularization with Dot-Product Kernels. NIPS 2000: 308-314 | |
| 27 | Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett: New Support Vector Algorithms. Neural Computation 12(5): 1207-1245 (2000) | |
| 1999 | ||
| 26 | Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson: Covering Numbers for Support Vector Machines. COLT 1999: 267-277 | |
| 25 | Alex J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf: Regularized Principal Manifolds. EuroCOLT 1999: 214-229 | |
| 24 | Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Entropy Numbers, Operators and Support Vector Kernels. EuroCOLT 1999: 285-299 | |
| 23 | Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson: The Entropy Regularization Information Criterion. NIPS 1999: 342-348 | |
| 22 | Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt: Support Vector Method for Novelty Detection. NIPS 1999: 582-588 | |
| 1998 | ||
| 21 | Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson: Shrinking the Tube: A New Support Vector Regression Algorithm. NIPS 1998: 330-336 | |
| 20 | 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) | |
| 19 | 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) | |
| 1997 | ||
| 18 | John Shawe-Taylor, Robert C. Williamson: A PAC Analysis of a Bayesian Estimator. COLT 1997: 2-9 | |
| 17 | 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) | |
| 16 | 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) | |
| 1996 | ||
| 15 | Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: The Importance of Convexity in Learning with Squared Loss. COLT 1996: 140-146 | |
| 14 | John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony: A Framework for Structural Risk Minimisation. COLT 1996: 68-76 | |
| 13 | 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) | |
| 12 | 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) | |
| 1995 | ||
| 11 | Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels, William A. Sethares: Online Learning via Congregational Gradient Descent. COLT 1995: 265-272 | |
| 10 | Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: On Efficient Agnostic Learning of Linear Combinations of Basis Functions. COLT 1995: 369-376 | |
| 9 | Adam Kowalczyk, Jacek Szymanski, Peter L. Bartlett, Robert C. Williamson: Examples of learning curves from a modified VC-formalism. NIPS 1995: 344-350 | |
| 1994 | ||
| 8 | Peter L. Bartlett, Philip M. Long, Robert C. Williamson: Fat-Shattering and the Learnability of Real-Valued Functions. COLT 1994: 299-310 | |
| 7 | Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes. COLT 1994: 362-367 | |
| 1992 | ||
| 6 | Uwe Helmke, Robert C. Williamson: Rational Parametrizations of Neural Networks. NIPS 1992: 623-630 | |
| 1991 | ||
| 5 | Peter L. Bartlett, Robert C. Williamson: Investigating the Distribution Assumptions in the Pac Learning Model. COLT 1991: 24-32 | |
| 4 | Robert C. Williamson, Peter L. Bartlett: Splines, Rational Functions and Neural Networks. NIPS 1991: 1040-1047 | |
| 3 | Robert C. Williamson: An extreme limit theorem for dependency bounds of normalized sums of random variables. Inf. Sci. 56(1-3): 113-141 (1991) | |
| 1990 | ||
| 2 | Robert C. Williamson: epsilon-Entropy and the Complexity of Feedforward Neural Networks. NIPS 1990: 946-952 | |
| 1 | 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) | |