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ORCIDRobert C. Williamson
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- affiliation: Universitaet Tuebingen, Germany
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2020 – today
- 2024
[j56]Christian Fröhlich
, Rabanus Derr, Robert C. Williamson:
Strictly frequentist imprecise probability. Int. J. Approx. Reason. 168: 109148 (2024)- [j55]Robert C. Williamson, Zac Cranko:
Information Processing Equalities and the Information-Risk Bridge. J. Mach. Learn. Res. 25: 103:1-103:53 (2024) - [j54]Christian Fröhlich, Robert C. Williamson:
Risk Measures and Upper Probabilities: Coherence and Stratification. J. Mach. Learn. Res. 25: 207:1-207:100 (2024) - [c77]Christian Fröhlich, Robert C. Williamson:
Insights From Insurance for Fair Machine Learning. FAccT 2024: 407-421 - [i37]Rabanus Derr, Robert C. Williamson:
Four Facets of Forecast Felicity: Calibration, Predictiveness, Randomness and Regret. CoRR abs/2401.14483 (2024) - [i36]Facundo Mémoli, Brantley Vose, Robert C. Williamson:
Geometry and Stability of Supervised Learning Problems. CoRR abs/2403.01660 (2024) - [i35]Armando J. Cabrera Pacheco, Rabanus Derr, Robert C. Williamson:
An Axiomatic Approach to Loss Aggregation and an Adapted Aggregating Algorithm. CoRR abs/2406.02292 (2024) - [i34]Mina Remeli, Moritz Hardt, Robert C. Williamson:
Limits to Predicting Online Speech Using Large Language Models. CoRR abs/2407.12850 (2024) - [i33]Benedikt Höltgen, Robert C. Williamson:
Causal modelling without counterfactuals and individualised effects. CoRR abs/2407.17385 (2024) - [i32]Benedikt Höltgen, Robert C. Williamson:
Five reasons against assuming a data-generating distribution in Machine Learning. CoRR abs/2407.17395 (2024) - [i31]Christian Fröhlich, Robert C. Williamson:
Scoring Rules and Calibration for Imprecise Probabilities. CoRR abs/2410.23001 (2024) - 2023
- [j53]Rabanus Derr, Robert C. Williamson:
Systems of Precision: Coherent Probabilities on Pre-Dynkin Systems and Coherent Previsions on Linear Subspaces. Entropy 25(9): 1283 (2023) - [j52]Robert C. Williamson, Zac Cranko:
The Geometry and Calculus of Losses. J. Mach. Learn. Res. 24: 342:1-342:72 (2023) - [j51]Christian Fröhlich, Robert C. Williamson:
Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity. Trans. Mach. Learn. Res. 2023 (2023) - [j50]Armando J. Cabrera Pacheco, Robert C. Williamson:
The Geometry of Mixability. Trans. Mach. Learn. Res. 2023 (2023) - [c76]Benedikt Höltgen, Robert C. Williamson:
On the Richness of Calibration. FAccT 2023: 1124-1138 - [c75]Yishay Mansour, Richard Nock, Robert C. Williamson:
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice. ICML 2023: 23706-23742 - [c74]Rabanus Derr, Robert C. Williamson:
The set structure of precision. ISIPTA 2023: 165-176 - [c73]Christian Fröhlich, Rabanus Derr, Robert C. Williamson:
Towards a strictly frequentist theory of imprecise probability. ISIPTA 2023: 230-240 - [i30]Benedikt Höltgen, Robert C. Williamson:
On the Richness of Calibration. CoRR abs/2302.04118 (2023) - [i29]Armando J. Cabrera Pacheco, Robert C. Williamson:
The Geometry of Mixability. CoRR abs/2302.11905 (2023) - [i28]Christian Fröhlich, Robert C. Williamson:
Insights From Insurance for Fair Machine Learning: Responsibility, Performativity and Aggregates. CoRR abs/2306.14624 (2023) - [i27]Laura Iacovissi, Nan Lu, Robert C. Williamson:
A General Framework for Learning under Corruption: Label Noise, Attribute Noise, and Beyond. CoRR abs/2307.08643 (2023) - 2022
- [j49]Lachlan McCalman, Daniel Steinberg, Grace Abuhamad, Marc-Etienne Brunet, Robert C. Williamson, Richard S. Zemel:
Assessing AI Fairness in Finance. Computer 55(1): 94-97 (2022) - [i26]Yishay Mansour, Richard Nock, Robert C. Williamson:
What killed the Convex Booster ? CoRR abs/2205.09628 (2022) - [i25]Christian Fröhlich, Robert C. Williamson:
Risk Measures and Upper Probabilities: Coherence and Stratification. CoRR abs/2206.03183 (2022) - [i24]Robert C. Williamson, Zac Cranko:
Information Processing Equalities and the Information-Risk Bridge. CoRR abs/2207.11987 (2022) - [i23]Rabanus Derr, Robert C. Williamson:
Fairness and Randomness in Machine Learning: Statistical Independence and Relativization. CoRR abs/2207.13596 (2022) - [i22]Christian Fröhlich, Robert C. Williamson:
Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity. CoRR abs/2208.03066 (2022) - [i21]Robert C. Williamson, Zac Cranko:
The Geometry and Calculus of Losses. CoRR abs/2209.00238 (2022) - 2020
- [c72]Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson:
PAC-Bayesian Bound for the Conditional Value at Risk. NeurIPS 2020 - [i20]Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson:
PAC-Bayesian Bound for the Conditional Value at Risk. CoRR abs/2006.14763 (2020)
2010 – 2019
- 2019
- [c71]Daniel McNamara, Cheng Soon Ong, Robert C. Williamson:
Costs and Benefits of Fair Representation Learning. AIES 2019: 263-270 - [c70]Richard Nock, Robert C. Williamson:
Lossless or Quantized Boosting with Integer Arithmetic. ICML 2019: 4829-4838 - [c69]Robert C. Williamson, Aditya Krishna Menon:
Fairness risk measures. ICML 2019: 6786-6797 - [c68]Hisham Husain, Richard Nock, Robert C. Williamson:
A Primal-Dual link between GANs and Autoencoders. NeurIPS 2019: 413-422 - [i19]Robert C. Williamson, Aditya Krishna Menon:
Fairness risk measures. CoRR abs/1901.08665 (2019) - [i18]Hisham Husain, Richard Nock, Robert C. Williamson:
Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds. CoRR abs/1902.00985 (2019) - 2018
- [c67]Aditya Krishna Menon, Robert C. Williamson:
The cost of fairness in binary classification. FAT 2018: 107-118 - [c66]Zakaria Mhammedi, Robert C. Williamson:
Constant Regret, Generalized Mixability, and Mirror Descent. NeurIPS 2018: 7430-7439 - [i17]Zakaria Mhammedi, Robert C. Williamson:
Constant Regret, Generalized Mixability, and Mirror Descent. CoRR abs/1802.06965 (2018) - [i16]Parameswaran Kamalaruban, Robert C. Williamson:
Minimax Lower Bounds for Cost Sensitive Classification. CoRR abs/1805.07723 (2018) - [i15]Parameswaran Kamalaruban, Robert C. Williamson, Xinhua Zhang:
Exp-Concavity of Proper Composite Losses. CoRR abs/1805.07737 (2018) - 2017
- [j48]Brendan van Rooyen, Robert C. Williamson:
A Theory of Learning with Corrupted Labels. J. Mach. Learn. Res. 18: 228:1-228:50 (2017) - [c65]Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson:
f-GANs in an Information Geometric Nutshell. NIPS 2017: 456-464 - [i14]Aditya Krishna Menon, Robert C. Williamson:
The cost of fairness in classification. CoRR abs/1705.09055 (2017) - [i13]Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson:
f-GANs in an Information Geometric Nutshell. CoRR abs/1707.04385 (2017) - [i12]Daniel McNamara, Cheng Soon Ong, Robert C. Williamson:
Provably Fair Representations. CoRR abs/1710.04394 (2017) - 2016
- [j47]Aditya Krishna Menon, Robert C. Williamson:
Bipartite Ranking: a Risk-Theoretic Perspective. J. Mach. Learn. Res. 17: 195:1-195:102 (2016) - [j46]Robert C. Williamson, Elodie Vernet, Mark D. Reid:
Composite Multiclass Losses. J. Mach. Learn. Res. 17: 223:1-223:52 (2016) - [i11]Daniel McNamara, Cheng Soon Ong, Robert C. Williamson:
A Modular Theory of Feature Learning. CoRR abs/1611.03125 (2016) - 2015
- [j45]Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson:
Fast rates in statistical and online learning. J. Mach. Learn. Res. 16: 1793-1861 (2015) - [c64]Parameswaran Kamalaruban, Robert C. Williamson, Xinhua Zhang:
Exp-Concavity of Proper Composite Losses. COLT 2015: 1035-1065 - [c63]Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson, Nishant A. Mehta:
Generalized Mixability via Entropic Duality. COLT 2015: 1501-1522 - [c62]Brendan van Rooyen, Aditya Krishna Menon, Robert C. Williamson:
Learning with Symmetric Label Noise: The Importance of Being Unhinged. NIPS 2015: 10-18 - [i10]Brendan van Rooyen, Robert C. Williamson:
A Theory of Feature Learning. CoRR abs/1504.00083 (2015) - [i9]Brendan van Rooyen, Robert C. Williamson:
Learning in the Presence of Corruption. CoRR abs/1504.00091 (2015) - [i8]Brendan van Rooyen, Aditya Krishna Menon, Robert C. Williamson:
Learning with Symmetric Label Noise: The Importance of Being Unhinged. CoRR abs/1505.07634 (2015) - [i7]Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson:
Fast rates in statistical and online learning. CoRR abs/1507.02592 (2015) - 2014
- [c61]Aditya Krishna Menon, Robert C. Williamson:
Bayes-Optimal Scorers for Bipartite Ranking. COLT 2014: 68-106 - [c60]Ingo Steinwart, Chloé Pasin, Robert C. Williamson, Siyu Zhang:
Elicitation and Identification of Properties. COLT 2014: 482-526 - [c59]Harish G. Ramaswamy, Balaji Srinivasan Babu, Shivani Agarwal, Robert C. Williamson:
On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems. COLT 2014: 885-902 - [c58]Robert C. Williamson:
The Geometry of Losses. COLT 2014: 1078-1108 - [c57]Nishant A. Mehta, Robert C. Williamson:
From Stochastic Mixability to Fast Rates. NIPS 2014: 1197-1205 - [i6]Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson:
Generalised Mixability, Constant Regret, and Bayesian Updating. CoRR abs/1403.2433 (2014) - [i5]Nishant A. Mehta, Robert C. Williamson:
From Stochastic Mixability to Fast Rates. CoRR abs/1406.3781 (2014) - [i4]Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson, Nishant A. Mehta:
Generalized Mixability via Entropic Duality. CoRR abs/1406.6130 (2014) - 2013
- [c56]Robert C. Williamson:
Loss Functions. Empirical Inference 2013: 71-80 - 2012
- [j44]Tim van Erven, Mark D. Reid, Robert C. Williamson:
Mixability is Bayes Risk Curvature Relative to Log Loss. J. Mach. Learn. Res. 13: 1639-1663 (2012) - [c55]Mark D. Reid, Robert C. Williamson, Peng Sun:
The Convexity and Design of Composite Multiclass Losses. ICML 2012 - [c54]Tim van Erven, Peter D. Grünwald, Mark D. Reid, Robert C. Williamson:
Mixability in Statistical Learning. NIPS 2012: 1700-1708 - [c53]Dario García-García, Robert C. Williamson:
Divergences and Risks for Multiclass Experiments. COLT 2012: 28.1-28.20 - [c52]Ulrike von Luxburg, Robert C. Williamson, Isabelle Guyon:
Clustering: Science or Art? ICML Unsupervised and Transfer Learning 2012: 65-80 - [e2]Shie Mannor, Nathan Srebro, Robert C. Williamson:
COLT 2012 - The 25th Annual Conference on Learning Theory, June 25-27, 2012, Edinburgh, Scotland. JMLR Proceedings 23, JMLR.org 2012 [contents] - [i3]Mark D. Reid, Robert C. Williamson, Peng Sun:
The Convexity and Design of Composite Multiclass Losses. CoRR abs/1206.4663 (2012) - [i2]Ayman Ghoneim, Robert C. Williamson:
Strategy-Proof Prediction Markets. CoRR abs/1212.5764 (2012) - 2011
- [j43]Mark D. Reid, Robert C. Williamson:
Information, Divergence and Risk for Binary Experiments. J. Mach. Learn. Res. 12: 731-817 (2011) - [c51]Elodie Vernet, Robert C. Williamson, Mark D. Reid:
Composite Multiclass Losses. NIPS 2011: 1224-1232 - [c50]Tim van Erven, Mark D. Reid, Robert C. Williamson:
Mixability is Bayes Risk Curvature Relative to Log Loss. COLT 2011: 233-252 - 2010
- [j42]Mark D. Reid, Robert C. Williamson:
Composite Binary Losses. J. Mach. Learn. Res. 11: 2387-2422 (2010) - [c49]Mark D. Reid, Robert C. Williamson:
Convexity of Proper Composite Binary Losses. AISTATS 2010: 637-644
2000 – 2009
- 2009
- [c48]Mark D. Reid, Robert C. Williamson:
Generalised Pinsker Inequalities. COLT 2009 - [c47]Mark D. Reid, Robert C. Williamson:
Surrogate regret bounds for proper losses. ICML 2009: 897-904 - [i1]Mark D. Reid, Robert C. Williamson:
Generalised Pinsker Inequalities. CoRR abs/0906.1244 (2009) - 2008
- [j41]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
Correction to "The Importance of Convexity in Learning With Squared Loss". IEEE Trans. Inf. Theory 54(9): 4395 (2008) - 2007
- [j40]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. J. Mach. Learn. Res. 8: 2443-2466 (2007) - 2006
- [j39]Eric A. Lehmann, Robert C. Williamson:
Particle Filter Design Using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environments. EURASIP J. Adv. Signal Process. 2006 (2006) - 2005
- [j38]Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson:
Learning the Kernel with Hyperkernels. J. Mach. Learn. Res. 6: 1043-1071 (2005) - [c46]Omri Guttman, S. V. N. Vishwanathan, Robert C. Williamson:
Learnability of Probabilistic Automata via Oracles. ALT 2005: 171-182 - 2004
- [j37]Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson:
Online learning with kernels. IEEE Trans. Signal Process. 52(8): 2165-2176 (2004) - 2003
- [j36]Darren B. Ward, Eric A. Lehmann, Robert C. Williamson:
Particle filtering algorithms for tracking an acoustic source in a reverberant environment. IEEE Trans. Speech Audio Process. 11(6): 826-836 (2003) - [c45]Eric A. Lehmann, Darren B. Ward, Robert C. Williamson:
Experimental comparison of particle filtering algorithms for acoustic source localization in a reverberant room. ICASSP (5) 2003: 177-180 - [c44]Terence Betlehem, Robert C. Williamson:
Acoustic beamforming exploiting directionality of human speech sources. ICASSP (5) 2003: 365-368 - [c43]Edward Harrington, Jyrki Kivinen, Robert C. Williamson:
Channel equalization and the Bayes point machine. ICASSP (4) 2003: 493-496 - [c42]James A. McGowan, Robert C. Williamson:
Loop removal from LDPC codes. ITW 2003: 230-233 - [c41]Edward Harrington, Ralf Herbrich, Jyrki Kivinen, John C. Platt, Robert C. Williamson:
Online Bayes Point Machines. PAKDD 2003: 241-252 - 2002
- [j35]Ralf Herbrich, Robert C. Williamson:
Algorithmic Luckiness. J. Mach. Learn. Res. 3: 175-212 (2002) - [j34]Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson:
Covering numbers for support vector machines. IEEE Trans. Inf. Theory 48(1): 239-250 (2002) - [j33]Richard K. Martin, William A. Sethares, Robert C. Williamson, C. Richard Johnson Jr.:
Exploiting sparsity in adaptive filters. IEEE Trans. Signal Process. 50(8): 1883-1894 (2002) - [c40]Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson:
Large Margin Classification for Moving Targets. ALT 2002: 113-127 - [c39]Shahar Mendelson, Robert C. Williamson:
Agnostic Learning Nonconvex Function Classes. COLT 2002: 1-13 - [c38]Darren B. Ward, Robert C. Williamson:
Particle filter beamforming for acoustic source localization in a reverberant environment. ICASSP 2002: 1777-1780 - [c37]Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson:
Hyperkernels. NIPS 2002: 478-485 - 2001
- [j32]Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson:
Regularized Principal Manifolds. J. Mach. Learn. Res. 1: 179-209 (2001) - [j31]Robert E. Mahony, Robert C. Williamson:
Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms. J. Mach. Learn. Res. 1: 311-355 (2001) - [j30]Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alexander J. Smola, Robert C. Williamson:
Estimating the Support of a High-Dimensional Distribution. Neural Comput. 13(7): 1443-1471 (2001) - [j29]Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf:
Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. IEEE Trans. Inf. Theory 47(6): 2516-2532 (2001) - [j28]Simon I. Hill, Robert C. Williamson:
Convergence of exponentiated gradient algorithms. IEEE Trans. Signal Process. 49(6): 1208-1215 (2001) - [c36]Marshall Shepard, Robert C. Williamson:
Very low voltage power conversion. ISCAS (3) 2001: 289-292 - [c35]Ralf Herbrich, Robert C. Williamson:
Algorithmic Luckiness. NIPS 2001: 391-397 - [c34]Adam Kowalczyk, Alexander J. Smola, Robert C. Williamson:
Kernel Machines and Boolean Functions. NIPS 2001: 439-446 - [c33]Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson:
Online Learning with Kernels. NIPS 2001: 785-792 - [p1]Darren B. Ward, Rodney A. Kennedy, Robert C. Williamson:
Constant Directivity Beamforming. Microphone Arrays 2001: 3-17 - [e1]David P. Helmbold, Robert C. Williamson:
Computational Learning Theory, 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, The Netherlands, July 16-19, 2001, Proceedings. Lecture Notes in Computer Science 2111, Springer 2001, ISBN 3-540-42343-5 [contents] - 2000
- [j27]Bernhard Schölkopf, Alexander J. Smola, Robert C. Williamson, Peter L. Bartlett:
New Support Vector Algorithms. Neural Comput. 12(5): 1207-1245 (2000) - [j26]Biljana D. Radlovic, Robert C. Williamson, Rodney A. Kennedy:
Equalization in an acoustic reverberant environment: robustness results. IEEE Trans. Speech Audio Process. 8(3): 311-319 (2000) - [c32]Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf:
Entropy Numbers of Linear Function Classes. COLT 2000: 309-319 - [c31]Paul D. Teal, Robert C. Williamson, Rodney A. Kennedy:
Error performance of a channel of known impulse response. ICASSP 2000: 2733-2736 - [c30]Thore Graepel, Ralf Herbrich, Robert C. Williamson:
From Margin to Sparsity. NIPS 2000: 210-216 - [c29]Alexander J. Smola, Zoltán L. Óvári, Robert C. Williamson:
Regularization with Dot-Product Kernels. NIPS 2000: 308-314
1990 – 1999
- 1999
- [j25]Thushara D. Abhayapala, Rodney A. Kennedy, Robert C. Williamson:
Noise modeling for nearfield array optimization. IEEE Signal Process. Lett. 6(8): 210-212 (1999) - [j24]Erik Weyer, Robert C. Williamson, Iven M. Y. Mareels:
Finite sample properties of linear model identification. IEEE Trans. Autom. Control. 44(7): 1370-1383 (1999) - [c28]Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson:
Covering Numbers for Support Vector Machines. COLT 1999: 267-277 - [c27]Alexander J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf:
Regularized Principal Manifolds. EuroCOLT 1999: 214-229 - [c26]Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf:
Entropy Numbers, Operators and Support Vector Kernels. EuroCOLT 1999: 285-299 - [c25]Biljana D. Radlovic, Robert C. Williamson, Rodney A. Kennedy:
On the poor robustness of sound equalization in reverberant environments. ICASSP 1999: 881-884 - [c24]Simon I. Hill, Robert C. Williamson:
An analysis of the exponentiated gradient descent algorithm. ISSPA 1999: 379-382 - [c23]Thushara D. Abhayapala, Rodney A. Kennedy, Robert C. Williamson, Darren B. Ward:
Nearfield broadband adaptive beamforming. ISSPA 1999: 839-842 - [c22]Darren B. Ward, Robert C. Williamson:
Beamforming for a source located in the interior of a sensor array. ISSPA 1999: 873-876 - [c21]Alexander J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson:
The Entropy Regularization Information Criterion. NIPS 1999: 342-348 - [c20]Bernhard Schölkopf, Robert C. Williamson, Alexander J. Smola, John Shawe-Taylor, John C. Platt:
Support Vector Method for Novelty Detection. NIPS 1999: 582-588 - 1998
- [j23]Erik Weyer, Robert C. Williamson, Iven M. Y. Mareels:
On the Relationship Between Behavioural and Standard Methods for System Identification. Autom. 34(6): 801-804 (1998) - [j22]John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony:
Structural Risk Minimization Over Data-Dependent Hierarchies. IEEE Trans. Inf. Theory 44(5): 1926-1940 (1998) - [j21]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
The Importance of Convexity in Learning with Squared Loss. IEEE Trans. Inf. Theory 44(5): 1974-1980 (1998) - [c19]Thushara D. Abhayapala, Rodney A. Kennedy, Robert C. Williamson:
Broadband beamforming using elementary shape invariant beampatterns. ICASSP 1998: 2041-2044 - [c18]Bernhard Schölkopf, Peter L. Bartlett, Alexander J. Smola, Robert C. Williamson:
Shrinking the Tube: A New Support Vector Regression Algorithm. NIPS 1998: 330-336 - 1997
- [j20]Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels, William A. Sethares:
Online learning via congregational gradient descent. Math. Control. Signals Syst. 10(4): 331-363 (1997) - [j19]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
Correction to 'Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes'. Neural Comput. 9(4): 765-769 (1997) - [j18]Jennifer A. Fulton, Robert R. Bitmead, Robert C. Williamson:
Sampling rate versus quantisation in speech coders. Signal Process. 56(3): 209-218 (1997) - [j17]Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels:
Decision region approximation by polynomials or neural networks. IEEE Trans. Inf. Theory 43(3): 903-907 (1997) - [c17]John Shawe-Taylor, Robert C. Williamson:
A PAC Analysis of a Bayesian Estimator. COLT 1997: 2-9 - [c16]Darren B. Ward, Rodney A. Kennedy, Robert C. Williamson:
An adaptive algorithm for broadband frequency invariant beamforming. ICASSP 1997: 3737-3740 - 1996
- [j16]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) - [j15]Peter L. Bartlett, Robert C. Williamson:
The VC Dimension and Pseudodimension of Two-Layer Neural Networks with Discrete Inputs. Neural Comput. 8(3): 625-628 (1996) - [j14]Darren B. Ward, Rodney A. Kennedy, Robert C. Williamson:
FIR filter design for frequency invariant beamformers. IEEE Signal Process. Lett. 3(3): 69-71 (1996) - [j13]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
Efficient agnostic learning of neural networks with bounded fan-in. IEEE Trans. Inf. Theory 42(6): 2118-2132 (1996) - [c15]John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony:
A Framework for Structural Risk Minimisation. COLT 1996: 68-76 - [c14]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
The Importance of Convexity in Learning with Squared Loss. COLT 1996: 140-146 - [c13]Rodney A. Kennedy, Thushara D. Abhayapala, Darren B. Ward, Robert C. Williamson:
Nearfield broadband frequency invariant beamforming. ICASSP 1996: 905-908 - 1995
- [j12]Uwe Helmke, Robert C. Williamson:
Neural networks, rational functions, and realization theory. Math. Control. Signals Syst. 8(1): 27-49 (1995) - [j11]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
Lower Bounds on the VC Dimension of Smoothly Parameterized Function Classes. Neural Comput. 7(5): 1040-1053 (1995) - [j10]Robert C. Williamson, Uwe Helmke:
Existence and uniqueness results for neural network approximations. IEEE Trans. Neural Networks 6(1): 2-13 (1995) - [j9]Ben James, Brian D. O. Anderson, Robert C. Williamson:
Characterization of threshold for single tone maximum likelihood frequency estimation. IEEE Trans. Signal Process. 43(4): 817-821 (1995) - [j8]Mehmet Karan, Brian D. O. Anderson, Robert C. Williamson:
An efficient calculation of the moments of matched and mismatched hidden Markov models. IEEE Trans. Signal Process. 43(10): 2422-2425 (1995) - [c12]Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels, William A. Sethares:
Online Learning via Congregational Gradient Descent. COLT 1995: 265-272 - [c11]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
On Efficient Agnostic Learning of Linear Combinations of Basis Functions. COLT 1995: 369-376 - [c10]Adam Kowalczyk, J. Szymariski, Robert C. Williamson:
Learning curves from a modified VC-formalism: a case study. ICNN 1995: 2939-2943 - [c9]Adam Kowalczyk, Jacek Szymanski, Peter L. Bartlett, Robert C. Williamson:
Examples of learning curves from a modified VC-formalism. NIPS 1995: 344-350 - 1994
- [j7]Robert C. Williamson, Ben James, Brian D. O. Anderson, Peter J. Kootsookos:
Threshold effects in multiharmonic maximum likelihood frequency estimation. Signal Process. 37(3): 309-331 (1994) - [j6]Ben James, Brian D. O. Anderson, Robert C. Williamson:
Conditional mean and maximum likelihood approaches to multiharmonic frequency estimation. IEEE Trans. Signal Process. 42(6): 1366-1375 (1994) - [j5]Mehmet Karan, Robert C. Williamson, Brian D. O. Anderson:
Performance of the maximum likelihood constant frequency estimator for frequency tracking. IEEE Trans. Signal Process. 42(10): 2749-2757 (1994) - [c8]Peter L. Bartlett, Philip M. Long, Robert C. Williamson:
Fat-Shattering and the Learnability of Real-Valued Functions. COLT 1994: 299-310 - [c7]Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes. COLT 1994: 362-367 - [c6]Mehmet Karan, Brian D. O. Anderson, Robert C. Williamson:
A simple calculation of the joint moments of hidden Markov models. ICASSP (4) 1994: 333-336 - 1993
- [j4]Brian C. Lovell, Robert C. Williamson, Boualem Boashash:
The relationship between instantaneous frequency and time-frequency representations. IEEE Trans. Signal Process. 41(3): 1458-1461 (1993) - 1992
- [j3]Brian C. Lovell, Robert C. Williamson:
The statistical performance of some instantaneous frequency estimators. IEEE Trans. Signal Process. 40(7): 1708-1723 (1992) - [c5]Uwe Helmke, Robert C. Williamson:
Rational Parametrizations of Neural Networks. NIPS 1992: 623-630 - 1991
- [j2]Robert C. Williamson:
An extreme limit theorem for dependency bounds of normalized sums of random variables. Inf. Sci. 56(1-3): 113-141 (1991) - [c4]Peter L. Bartlett, Robert C. Williamson:
Investigating the Distribution Assumptions in the Pac Learning Model. COLT 1991: 24-32 - [c3]Brian C. Lovell, Peter J. Kootsookos, Robert C. Williamson:
The circular nature of discrete-time frequency estimates. ICASSP 1991: 3369-3372 - [c2]Robert C. Williamson, Peter L. Bartlett:
Splines, Rational Functions and Neural Networks. NIPS 1991: 1040-1047 - 1990
- [b1]Robert C. Williamson:
Probabilistic arithmetic. University of Queensland, Australia, 1990 - [j1]Robert C. Williamson, Tom Downs:
Probabilistic arithmetic. I. Numerical methods for calculating convolutions and dependency bounds. Int. J. Approx. Reason. 4(2): 89-158 (1990) - [c1]Robert C. Williamson:
epsilon-Entropy and the Complexity of Feedforward Neural Networks. NIPS 1990: 946-952
Coauthor Index
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