NIPS Proceedingsβ

David Barber

16 Papers

  • Affine Independent Variational Inference (2012)
  • A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes (2012)
  • A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems (2006)
  • Unified Inference for Variational Bayesian Linear Gaussian State-Space Models (2006)
  • Kernelized Infomax Clustering (2005)
  • Information Maximization in Noisy Channels : A Variational Approach (2003)
  • Dynamic Bayesian Networks with Deterministic Latent Tables (2002)
  • Learning in Spiking Neural Assemblies (2002)
  • Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks (1999)
  • Tractable Variational Structures for Approximating Graphical Models (1998)
  • Ensemble Learning for Multi-Layer Networks (1997)
  • On-line Learning from Finite Training Sets in Nonlinear Networks (1997)
  • Radial Basis Functions: A Bayesian Treatment (1997)
  • Bayesian Model Comparison by Monte Carlo Chaining (1996)
  • Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo (1996)
  • Online Learning from Finite Training Sets: An Analytical Case Study (1996)