Sollich, Peter

19 publications

JMLR 2013 Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs Matthew J. Urry, Peter Sollich

NeurIPS 2012 Learning Curves for Multi-Task Gaussian Process Regression Peter Sollich, Simon Ashton

NeurIPS 2010 Exact Learning Curves for Gaussian Process Regression on Large Random Graphs Matthew Urry, Peter Sollich

NeurIPS 2009 Kernels and Learning Curves for Gaussian Process Regression on Random Graphs Peter Sollich, Matthew Urry, Camille Coti

NeurIPS 2004 Using the Equivalent Kernel to Understand Gaussian Process Regression Peter Sollich, Christopher Williams

MLJ 2002 Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities Peter Sollich

NeCo 2002 Learning Curves for Gaussian Process Regression: Approximations and Bounds Peter Sollich, Anason S. Halees

NeurIPS 2001 Gaussian Process Regression with Mismatched Models Peter Sollich

NeurIPS 1999 Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks David Barber, Peter Sollich

NeurIPS 1999 Probabilistic Methods for Support Vector Machines Peter Sollich

NeurIPS 1998 Learning Curves for Gaussian Processes Peter Sollich

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

NeCo 1998 Online Learning from Finite Training Sets and Robustness to Input Bias Peter Sollich, David Barber

NeurIPS 1997 On-Line Learning from Finite Training Sets in Nonlinear Networks Peter Sollich, David Barber

NeurIPS 1996 Online Learning from Finite Training Sets: An Analytical Case Study Peter Sollich, David Barber

NeurIPS 1995 Learning with Ensembles: How Overfitting Can Be Useful Peter Sollich, Anders Krogh

NeCo 1995 Test Error Fluctuations in Finite Linear Perceptrons David Barber, David Saad, Peter Sollich

NeurIPS 1994 Learning from Queries for Maximum Information Gain in Imperfectly Learnable Problems Peter Sollich, David Saad

NeurIPS 1994 Learning in Large Linear Perceptrons and Why the Thermodynamic Limit Is Relevant to the Real World Peter Sollich