P Sollich.
Learning curves for Gaussian processes.
In M S Kearns, S A Solla, and D A Cohn, editors,
Advances in Neural Information Processing Systems 11,
pages 344-350, Cambridge, MA, 1999. MIT Press.
Abstract and full paper
P Sollich.
Approximate learning curves for Gaussian processes.
In ICANN99 - Ninth International Conference on Artificial
Neural Networks, pages 437-442, London, 1999. The Institution of
Electrical Engineers.
Abstract and full paper
P Sollich.
Probabilistic interpretation and Bayesian methods for Support Vector
Machines.
In ICANN99 - Ninth International Conference on Artificial
Neural Networks, pages 91-96, London, 1999. The Institution of
Electrical Engineers.
Abstract and full paper
P Sollich.
Probabilistic methods for Support Vector Machines.
In S A Solla, T K Leen and K-R Müller, editors,
Advances in Neural Information Processing Systems 12,
pages 349-355, Cambridge, MA, 2000. MIT Press.
Abstract and full paper
P Sollich.
Bayesian methods for Support Vector Machines: Evidence and predictive
class probabilities.
Machine Learning, 46:21-52, 2002.
Abstract and full paper
D Barber and P Sollich.
Gaussian fields for approximate inference in layered sigmoid belief networks.
In S A Solla, T K Leen and K-R Müller, editors,
Advances in Neural Information Processing Systems 12,
pages 393-399, Cambridge, MA, 2000. MIT Press.
Abstract and full paper
P Sollich and A Halees.
Learning curves for Gaussian process regression: Approximations and bounds.
Neural Computation, 14:1393-1428, 2002.
Abstract and full paper
P Sollich.
Generalization of Plaskota's bound for Gaussian process
learning curves.
Technical report, unpublished.
Abstract and full paper
P Sollich.
Gaussian process regression with mismatched models.
In T G Dietterich, S Becker and Z Ghahramani, editors,
Advances in Neural Information Processing Systems 14,
pages 519-526, Cambridge, MA, 2002. MIT Press.
Abstract and full paper
C Gold and P Sollich.
Model selection for Support Vector Machine classification.
Neurocomputing, 55:221-249, 2003.
Abstract and full paper
P Sollich and C K I Williams.
Using the equivalent kernel to understand Gaussian process regression.
In L K Saul, Y Weiss and L Bottou, editors,
Advances in Neural Information Processing Systems 17,
pages 1313-1320, Cambridge, MA, 2005. MIT Press.
Abstract and full paper
P Sollich and C K I Williams.
Understanding Gaussian process regression using the equivalent kernel.
In J Winkler, N Lawrence and M Niranjan, editors, Deterministic
and Statistical Methods in Machine Learning, Lecture Notes in
Artificial Intelligence 3635,
pages 199-210, Berlin, 2005. Springer.
Abstract and full paper
P Sollich.
Can Gaussian process regression be made robust against model mismatch?
In J Winkler, N Lawrence and M Niranjan, editors, Deterministic
and Statistical Methods in Machine Learning, Lecture Notes in
Artificial Intelligence 3635,
pages 211-228, Berlin, 2005. Springer.
Abstract and full paper
C Gold and P Sollich.
Fast Bayesian Support Vector Machine parameter tuning with the Nystrom
method.
In International Joint Conference on Neural
Networks (IJCNN) 2005, vols. 1-5, pages 2820-2825, New York, 2005. IEEE.
Abstract and full paper
C Gold, A Holub and P Sollich.
Bayesian approach to feature selection and parameter tuning for
Support Vector Machine classifiers.
Neural Networks, 18(5-6), 693-701, 2005.
Abstract and full paper
D Barber and P Sollich. Stable Belief Propagation in Gaussian DAGs.
In International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2007, vol. 2, pages 409-412, 2007. IEEE.
Abstract and full paper
P Sollich, M Urry and C Coti.
Kernels and learning curves for Gaussian process regression on random
graphs.
In Y Bengio, D Schuurmans, J Lafferty, C K I Williams and A Culotta, editors,
Advances in Neural Information Processing Systems 22,
pages 1723-1731, 2009.
Abstract and full paper
M Urry and P Sollich.
Exact learning curves for Gaussian process regression on large random graphs.
In J Lafferty and C K I Williams and J Shawe-Taylor and R S Zemel and
A Culotta, editors, Advances in Neural Information Processing Systems 23,
pages 2316-2324, 2010.
Abstract and full paper
M J Urry and P Sollich.
Replica theory for learning curves for Gaussian processes on random graphs.
Journal of Physics A, 45:425005, 2012.
Abstract and full paper
M J Urry and P Sollich.
Random walk kernels and learning curves for Gaussian process regression on random graphs.
Journal of Machine Learning Research, 14:1801-1835, 2013.
Abstract and full paper
P Sollich and S R F Ashton.
Learning curves for multi-task Gaussian process regression.
In P Bartlett, editor, Advances in Neural Information Processing
Systems 25, pages 1781-1789, 2013. Curran Associates.
Abstract and full paper
|
Last updated
Mon Oct 17 2016 Contact: Peter Sollich |
Back to my home page |