NIPS Proceedings
^{β}
Books
Manfred Opper
28 Papers
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding
(2015)
Optimal Neural Codes for Control and Estimation
(2014)
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data
(2014)
Approximate Gaussian process inference for the drift function in stochastic differential equations
(2013)
Approximate inference in latent Gaussian-Markov models from continuous time observations
(2013)
Analytical Results for the Error in Filtering of Gaussian Processes
(2011)
Inference in continuous-time change-point models
(2011)
Approximate inference in continuous time Gaussian-Jump processes
(2010)
Improving on Expectation Propagation
(2008)
Variational Inference for Diffusion Processes
(2007)
Variational inference for Markov jump processes
(2007)
An Approximate Inference Approach for the PCA Reconstruction Error
(2005)
Expectation Consistent Free Energies for Approximate Inference
(2004)
Approximate Analytical Bootstrap Averages for Support Vector Classifiers
(2003)
Variational Linear Response
(2003)
A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages
(2002)
Asymptotic Universality for Learning Curves of Support Vector Machines
(2001)
A Variational Approach to Learning Curves
(2001)
TAP Gibbs Free Energy, Belief Propagation and Sparsity
(2001)
Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations
(2000)
Sparse Representation for Gaussian Process Models
(2000)
Efficient Approaches to Gaussian Process Classification
(1999)
Finite-Dimensional Approximation of Gaussian Processes
(1998)
General Bounds on Bayes Errors for Regression with Gaussian Processes
(1998)
Mean Field Methods for Classification with Gaussian Processes
(1998)
A Mean Field Algorithm for Bayes Learning in Large Feed-forward Neural Networks
(1996)
Dynamics of Training
(1996)
Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods
(1991)