Learning in large linear perceptrons and why the thermodynamic limit is relevant to the real world. - Peter Sollich (NIPS, 1994)

Learning from queries for maximum information gain in imperfectly learnable problems. - Peter Sollich; David Saad (NIPS, 1994)

Minimum entropy queries for linear students learning nonlinear rules. - Peter Sollich (ESANN, 1995)

Learning with ensembles: How overfitting can be useful. - Peter Sollich; Anders Krogh (NIPS, 1995)

Test Error Fluctuations in Finite Linear Perceptrons. - David Barber; David Saad; Peter Sollich (Neural Comput., 1995)

Asking intelligent questions : the statistical mechanics of query learning. - Peter Sollich (1995)

Online Learning from Finite Training Sets: An Analytical Case Study. - Peter Sollich; David Barber (NIPS, 1996)

On-line Learning from Finite Training Sets in Nonlinear Networks. - Peter Sollich; David Barber (NIPS, 1997)

On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General Theories. - H. C. Rae; Peter Sollich; Anthony C. C. Coolen (NIPS, 1998)

Learning Curves for Gaussian Processes. - Peter Sollich (NIPS, 1998)

Online Learning from Finite Training Sets and Robustness to Input Bias. - Peter Sollich; David Barber (Neural Comput., 1998)

Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks. - David Barber; Peter Sollich (NIPS, 1999)

Probabilistic Methods for Support Vector Machines. - Peter Sollich (NIPS, 1999)

Gaussian Process Regression with Mismatched Models. - Peter Sollich (NIPS, 2001)

Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities. - Peter Sollich (Mach. Learn., 2002)

Learning Curves for Gaussian Process Regression: Approximations and Bounds. - Peter Sollich; Anason S. Halees (Neural Comput., 2002)

Model selection for support vector machine classification. - Carl Gold; Peter Sollich (Neurocomputing, 2003)

Can Gaussian Process Regression Be Made Robust Against Model Mismatch? - Peter Sollich (Deterministic and Statistical Methods in Machine Learning, 2004)

Understanding Gaussian Process Regression Using the Equivalent Kernel. - Peter Sollich; Christopher K. I. Williams (Deterministic and Statistical Methods in Machine Learning, 2004)

Using the Equivalent Kernel to Understand Gaussian Process Regression. - Peter Sollich; Christopher K. I. Williams (NIPS, 2004)

Fast Bayesian support vector machine parameter tuning with the Nystrom method. - Carl Gold; Peter Sollich (IJCNN, 2005)

Bayesian approach to feature selection and parameter tuning for support vector machine classifiers. - Carl Gold; Alex Holub; Peter Sollich (Neural Networks, 2005)

Robustness of phoneme classification in different representation spaces. - Lena Khoo; Zoran Cvetkovic; Peter Sollich (EUSIPCO, 2006)

Stable Belief Propagation in Gaussian Dags. - David Barber; Peter Sollich (ICASSP (2), 2007)

Phoneme Classification in Frequency Subbands using Ensemble Methods. - Nicholas Betteridge; Zoran Cvetkovic; Peter Sollich (DSP, 2007)

Towards robust phoneme classification: Augmentation of PLP models with acoustic waveforms. - Matthew Ager; Zoran Cvetkovic; Peter Sollich; Bin Yu 001 (EUSIPCO, 2008)

Combined PLP - Acoustic waveform classification for robust phoneme recognition using support vector machines. - Jibran Yousafzai; Zoran Cvetkovic; Peter Sollich; Bin Yu 001 (EUSIPCO, 2008)

Robust phoneme classification: Exploiting the adaptability of acoustic waveform models. - Matthew Ager; Zoran Cvetkovic; Peter Sollich (EUSIPCO, 2009)

Custom-designed SVM kernels for improved robustness of phoneme classification. - Jibran Yousafzai; Zoran Cvetkovic; Peter Sollich (EUSIPCO, 2009)

Tuning support vector machines for robust phoneme classification with acoustic waveforms. - Jibran Yousafzai; Zoran Cvetkovic; Peter Sollich (INTERSPEECH, 2009)

Kernels and learning curves for Gaussian process regression on random graphs. - Peter Sollich; Matthew Urry; Camille Coti (NIPS, 2009)

Towards robust phoneme classification with hybrid features. - Jibran Yousafzai; Zoran Cvetkovic; Peter Sollich (ISIT, 2010)

High-dimensional linear representations for robust speech recognition. - Matthew Ager; Zoran Cvetkovic; Peter Sollich (ITA, 2010)

Exact learning curves for Gaussian process regression on large random graphs. - Matthew Urry; Peter Sollich (NIPS, 2010)

Subband acoustic waveform front-end for robust speech recognition using support vector machines. - Jibran Yousafzai; Zoran Cvetkovic; Peter Sollich (SLT, 2010)

Combined waveform-cepstral representation for robust speech recognition. - Matthew Ager; Zoran Cvetkovic; Peter Sollich (ISIT, 2011)

Combined Features and Kernel Design for Noise Robust Phoneme Classification Using Support Vector Machines. - Jibran Yousafzai; Peter Sollich; Zoran Cvetkovic; Bin Yu 001 (IEEE Trans. Speech Audio Process., 2011)

Learning curves for multi-task Gaussian process regression. - Peter Sollich; Simon R. F. Ashton (NIPS, 2012)

Effects of domain-specific SVM kernel design on the robustness of automatic speech recognition. - Jibran Yousafzai; Zoran Cvetkovic; Peter Sollich (DSP, 2013)

Phoneme Classification in High-Dimensional Linear Feature Domains. - Matthew Ager; Zoran Cvetkovic; Peter Sollich (CoRR, 2013)

Random walk kernels and learning curves for Gaussian process regression on random graphs. - Matthew Urry; Peter Sollich (J. Mach. Learn. Res., 2013)

A Subband-Based SVM Front-End for Robust ASR. - Jibran Yousafzai; Zoran Cvetkovic; Peter Sollich; Matthew Ager (CoRR, 2014)

Phase transitions in Restricted Boltzmann Machines with generic priors. - Adriano Barra; Giuseppe Genovese; Peter Sollich; Daniele Tantari (CoRR, 2016)

Phase Diagram of Restricted Boltzmann Machines and Generalised Hopfield Networks with Arbitrary Priors. - Adriano Barra; Giuseppe Genovese; Daniele Tantari; Peter Sollich (CoRR, 2017)

Dynamical selection of Nash equilibria using Experience Weighted Attraction Learning: emergence of heterogeneous mixed equilibria. - Robin Nicole; Peter Sollich (CoRR, 2017)

Memory functions reveal structural properties of gene regulatory networks. - Edgar Herrera-Delgado; Ruben Perez-Carrasco; James Briscoe; Peter Sollich (PLoS Comput. Biol., 2018)

Parzen Filters for Spectral Decomposition of Signals. - Dino Oglic; Zoran Cvetkovic; Peter Sollich (CoRR, 2019)

Nonlinear memory functions capture and explain dynamical behaviours. - Edgar Herrera-Delgado; James Briscoe; Peter Sollich (CoRR, 2020)

Learning Waveform-Based Acoustic Models Using Deep Variational Convolutional Neural Networks. - Dino Oglic; Zoran Cvetkovic; Peter Sollich (IEEE ACM Trans. Audio Speech Lang. Process., 2021)

Towards Robust Waveform-Based Acoustic Models. - Dino Oglic; Zoran Cvetkovic; Peter Sollich; Steve Renals; Bin Yu 001 (IEEE ACM Trans. Audio Speech Lang. Process., 2022)

Data for Robust Prediction of Force Chains in Jammed Solids using Graph Neural Networks. - Rituparno Mandal; Corneel Casert; Peter Sollich (GRO.data, 2022)