NIPS Proceedingsβ

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)