P Sollich.
Learning in large linear perceptrons and why the thermodynamic limit
is relevant to the real world.
In G Tesauro, D S Touretzky, and T K Leen, editors, Advances in
Neural Information Processing Systems 7, pages 207-214, Cambridge, MA,
1995. MIT Press.
Abstract and full paper
P Sollich and D Saad.
Learning from queries for maximum information gain in imperfectly
learnable problems.
In G Tesauro, D S Touretzky, and T K Leen, editors, Advances in
Neural Information Processing Systems 7, pages 287-294, Cambridge, MA,
1995. MIT Press.
Abstract and full paper
P Sollich.
Minimum entropy queries for linear students learning nonlinear rules.
In Verleysen M, editor, Third European Symposium on Artificial
Neural Networks (ESANN'95), Proceedings, pages 217-222, Brussels, 1995. D
facto.
Abstract and full paper
P Sollich and A Krogh.
Learning with ensembles: When over-fitting can be useful.
In D S Touretzky, M C Mozer, and M E Hasselmo, editors, Advances
in Neural Information Processing Systems 8, pages 190-196, Cambridge, MA,
1996. MIT Press.
Abstract and full paper
P Sollich and D Barber.
Online learning from finite training sets: An analytical case study.
In M C Mozer and M I Jordan and T Petsche, editors,
Advances in Neural Information Processing Systems 9, pages 274-280,
Cambridge, MA, 1997. MIT Press.
Abstract and full paper
P Sollich.
Query learning for maximum information gain in a multi-layer neural
network.
In S W Ellacott, J C Mason, and I J Anderson, editors, Mathematics of Neural Networks: Models, Algorithms and
Applications, pages 339-343, Boston, MA, 1997. Kluwer
Academic.
Abstract and full paper
D Barber, P Sollich, and D Saad.
Finite size effects in on-line learning in multilayer neural
networks.
In S W Ellacott, J C Mason, and I J Anderson, editors, Mathematics of Neural Networks: Models, Algorithms and
Applications, pages 84-88, Boston, MA, 1997. Kluwer
Academic.
Abstract and full paper
M E Cates and P Sollich.
Rheology and glassy dynamics of foams.
In J F Sadoc and N Rivier, editors,
Foams and Emulsions, pages 207-236, Dordrecht, 1999. Kluwer Academic.
Abstract and full paper
P Sollich and D Barber.
Online learning from finite training sets in non-linear
networks.
In M I Jordan, M J Kearns, and S A Solla, editors,
Advances in Neural Information Processing Systems 10,
pages 357-363, Cambridge, MA, 1998. MIT Press.
Abstract and full paper
D Barber and P Sollich.
On-line learning from finite training sets.
In D Saad, editor, On-line learning in neural networks,
pages 279-302, Cambridge, 1998. Cambridge University Press.
Abstract and full paper
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
H C Rae, P Sollich, and A C C Coolen.
On-Line Learning with Restricted Training Sets: Exact Solution as
Benchmark for General Theories.
In M S Kearns, S A Solla, and D A Cohn, editors,
Advances in Neural Information Processing Systems 11,
pages 316-322, 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
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.
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
M Fasolo, P Sollich and A Speranza.
Phase equilibria in polydisperse colloidal systems.
React. Funct. Polym., 58:187-196, 2004.
Abstract and full paper
P Mayer and P Sollich.
Exact non-equilibrium fluctuation dissipation relations
for multi-spin observables in the Glauber-Ising spin chain.
Slow dynamics in complex systems, AIP Conference
Proceedings 708(1):703-704, 2004.
Abstract and full paper
S M Fielding and P Sollich.
Fluctuation-dissipation relations in ageing and driven non-mean
field glass models.
Slow dynamics in complex systems, AIP Conference
Proceedings 708(1):639-642, 2004.
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. Soft glassy rheology. In R G Weiss, P Terech, editors,
Molecular Gels: Materials with Self-Assembled Fibrillar Networks,
pages 161-192, Dordrecht, 2006. Springer.
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
N B Wilding and P Sollich.
Liquid-vapour phase behaviour of a polydisperse Lennard-Jones fluid.
Journal of Physics: Condensed Matter, 17(45):S3245-S3252, 2005.
Abstract and full paper
L Khoo, Z Cvetkovic, and P Sollich.
Robustness of phoneme classification in different representation spaces.
Proceedings of EUSIPCO 2006, Florence, Italy, September 4-8, 2006.
Abstract and full paper
Z Ghahramani, T L Griffiths, P Sollich.
Bayesian nonparametric latent feature models.
In J M Bernardo, M J Bayarri, J O Berger, A P Dawid,
D Heckerman, A F M Smith and M West, editors,
Bayesian Statistics 8, pages 201-225, 2007.
Oxford University Press.
Abstract and full paper
N Betteridge, Z Cvetkovic and P Sollich.
Phoneme classification in frequency subbands using ensemble methods.
In 15th International Conference on Digital Signal Processing,
pages 511-514, 2007. IEEE.
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
J Yousafzai, M Ager, Z Cvetkovic and P Sollich.
Discriminative and generative machine learning approaches towards
robust phoneme classification.
In Information Theory and Applications, 2008.
Abstract and full paper
M Ager, Z Cvetkovic, P Sollich and Bin Yu.
Towards robust phoneme classification:
Augmentation of PLP models with acoustic waveforms.
In Proceedings of European Signal Processing Conference, 2008.
Abstract and full paper
J Yousafzai, Z Cvetkovic, P Sollich and Bin Yu.
Combined PLP-acoustic waveform classification for robust
phoneme recognition using Support Vector Machines.
In Proceedings of European Signal Processing Conference, 2008.
Abstract and full paper
M Ager, Z Cvetkovic and P Sollich.
Robust phoneme classification: Exploiting the adaptability of acoustic
waveform models.
In Proceedings of European Signal Processing Conference, 2009.
Abstract and full paper
J Yousafzai, Z Cvetkovic and P Sollich.
Custom-designed SVM kernels for improved robustness of phoneme
classification.
In Proceedings of European Signal Processing Conference, 2009.
Abstract and full paper
J Yousafzai, Z Cvetkovic and P Sollich.
Tuning Support Vector Machines for robust phoneme classification with
acoustic waveforms.
In Proceedings of Interspeech 2009: 10th Annual Conference of the
International Speech Communication Association, pages 2359-2362, 2009.
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 Ager, Z Cvetkovic and P Sollich.
High-dimensional linear representations for robust speech recognition.
In 2010 Information Theory and Applications Workshop (ITA 2010),
pages 1-5, 2010.
Abstract and full paper
P Sollich and R L Jack.
Duality symmetries in driven one-dimensional hopping models.
Progress of Theoretical Physics Supplement, 184:200-210, 2010.
Abstract and full paper
R L Jack and P Sollich.
Large deviations and ensembles of trajectories in stochastic models.
Progress of Theoretical Physics Supplement, 184:304-317, 2010.
Abstract and full paper
J P Garrahan, P Sollich and C Toninelli.
Kinetically constrained models.
In L Berthier, G Biroli, J-P Bouchaud, L Cipelletti and W van
Saarloos, editors, pages 341-369, Oxford University Press, 2011.
Abstract and full paper
J Yousafzai, Z Cvetkovic and P Sollich.
Towards robust phoneme classification with hybrid features.
In 2010 IEEE International Symposium on Information Theory
Proceedings (ISIT), pages 1643-1647, 2010.
Abstract and full paper
J Yousafzai, Z Cvetkovic and P Sollich.
Subband acoustic waveform front-end for robust speech recognition
using support vector machines.
In 2010 IEEE Spoken Language Technology Workshop (SLT),
pages 253-258, 2010.
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 Ager, Z Cvetkovic and P Sollich.
Combined waveform-cepstral representation for robust speech recognition.
2011 IEEE International Symposium on Information Theory Proceedings
(ISIT), pages 864-868, 2011.
Abstract and full paper
J K Yousafzai, Z Cvetkovic, M Ager and P Sollich.
Redundancy in speech signals and robustness of automatic speech recognition.
XIII International Symposium on Problems of Redundancy in Information and Control Systems (RED), pages 93-98, 2012.
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
M Pica Ciamarra and P Sollich.
Density anomalies and high-order jamming crossovers.
4th International Symposium on Slow Dynamics in Complex Systems,
AIP Conf. Proc., 1518:176-180, 2013.
Abstract and full paper
J K Yousafzai, Z Cvetkovic and P Sollich.
Effects of domain-specific SVM kernel design on the robustness of
automatic speech recognition.
In DSP2013, Proceedings of 18th International Conference on
Digital Signal Processing, 2013.
Abstract and full paper
M P Ciamarra and P Sollich.
Elastic models of the glass transition applied to a liquid with density anomalies. In: Proceedings of 7th International Discussion Meeting on Relaxations in Complex Systems (IDMRCS),
Journal of Non-Crystalline Solids, 407:23-28, 2015.
Abstract and full paper
A Aloric, P Sollich and P McBurney.
Spontaneous Segregation of Agents Across Double Auction Markets.
In F Amblard, F J Miguel, A Blanchet and B Gaudou, editors,
Advances in Artificial Economics,
Lecture Notes in Economics and Mathematical Systems, 676:79-90, 2015.
Abstract and full paper
