Robert C. Williamson

2008
46	EE	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
45	EE	Omri Guttman, S. V. N. Vishwanathan, Robert C. Williamson: Learnability of Probabilistic Automata via Oracles. ALT 2005: 171-182
44	EE	Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson: Learning the Kernel with Hyperkernels. Journal of Machine Learning Research 6: 1043-1071 (2005)
2003
43	EE	Edward Harrington, Ralf Herbrich, Jyrki Kivinen, John C. Platt, Robert C. Williamson: Online Bayes Point Machines. PAKDD 2003: 241-252
2002
42	EE	Jyrki Kivinen, Alex J. Smola, Robert C. Williamson: Large Margin Classification for Moving Targets. ALT 2002: 113-127
41	EE	Shahar Mendelson, Robert C. Williamson: Agnostic Learning Nonconvex Function Classes. COLT 2002: 1-13
40	EE	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	EE	Ralf Herbrich, Robert C. Williamson: Algorithmic Luckiness. Journal of Machine Learning Research 3: 175-212 (2002)
2001
37	EE	Ralf Herbrich, Robert C. Williamson: Algorithmic Luckiness. NIPS 2001: 391-397
36	EE	Adam Kowalczyk, Alex J. Smola, Robert C. Williamson: Kernel Machines and Boolean Functions. NIPS 2001: 439-446
35	EE	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	EE	Alex J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson: Regularized Principal Manifolds. Journal of Machine Learning Research 1: 179-209 (2001)
32	EE	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	EE	Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson: Covering Numbers for Support Vector Machines. COLT 1999: 267-277
25	EE	Alex J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf: Regularized Principal Manifolds. EuroCOLT 1999: 214-229
24	EE	Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Entropy Numbers, Operators and Support Vector Kernels. EuroCOLT 1999: 285-299
23	EE	Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson: The Entropy Regularization Information Criterion. NIPS 1999: 342-348
22	EE	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	EE	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	EE	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	EE	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	EE	Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: The Importance of Convexity in Learning with Squared Loss. COLT 1996: 140-146
14	EE	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	EE	Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels, William A. Sethares: Online Learning via Congregational Gradient Descent. COLT 1995: 265-272
10	EE	Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: On Efficient Agnostic Learning of Linear Combinations of Basis Functions. COLT 1995: 369-376
9	EE	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	EE	Peter L. Bartlett, Philip M. Long, Robert C. Williamson: Fat-Shattering and the Learnability of Real-Valued Functions. COLT 1994: 299-310
7	EE	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	EE	Uwe Helmke, Robert C. Williamson: Rational Parametrizations of Neural Networks. NIPS 1992: 623-630
1991
5	EE	Peter L. Bartlett, Robert C. Williamson: Investigating the Distribution Assumptions in the Pac Learning Model. COLT 1991: 24-32
4	EE	Robert C. Williamson, Peter L. Bartlett: Splines, Rational Functions and Neural Networks. NIPS 1991: 1040-1047
3	EE	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	EE	Robert C. Williamson: epsilon-Entropy and the Complexity of Feedforward Neural Networks. NIPS 1990: 946-952
1	EE	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)

Coauthor Index

1	Martin Anthony	[14] [20]
2	Peter L. Bartlett	[4] [5] [7] [8] [9] [10] [12] [13] [14] [15] [16] [19] [20] [21] [26] [27] [39] [46]
3	Kim L. Blackmore	[11] [17]
4	Tom Downs	[1]
5	Thore Graepel	[29]
6	Ying Guo	[26] [39]
7	Omri Guttman	[45]
8	Edward Harrington	[43]
9	Uwe Helmke	[6]
10	Ralf Herbrich	[29] [37] [38] [43]
11	Jyrki Kivinen	[35] [42] [43]
12	Adam Kowalczyk	[9] [36]
13	Wee Sun Lee	[7] [10] [13] [15] [16] [19] [46]
14	Philip M. Long	[8] [12]
15	Robert E. Mahony	[32]
16	Iven M. Y. Mareels	[11] [17]
17	Shahar Mendelson	[41]
18	Sebastian Mika	[25] [33]
19	Cheng Soon Ong	[40] [44]
20	Zoltán L. Óvári	[28]
21	John C. Platt	[22] [31] [43]
22	Bernhard Schölkopf	[21] [22] [23] [24] [25] [27] [30] [31] [33] [34]
23	William A. Sethares	[11]
24	John Shawe-Taylor	[14] [18] [20] [22] [23] [26] [31] [39]
25	Alexander J. Smola (Alex J. Smola)	[21] [22] [23] [24] [25] [27] [28] [30] [31] [33] [34] [35] [36] [40] [42] [44]
26	Jacek Szymanski	[9]
27	S. V. N. Vishwanathan (Vishy Vishwanathan)	[45]

Colors in the list of coauthors