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All publications by Manfred Opper
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A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding
Yuval Harel, Ron Meir and Manfred Opper
Advances in Neural Information Processing Systems 28, 2015


Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data
Florian Stimberg, Andreas Ruttor and Manfred Opper
Advances in Neural Information Processing Systems 27, 2014


Optimal Neural Codes for Control and Estimation
Alex K. Susemihl, Ron Meir and Manfred Opper
Advances in Neural Information Processing Systems 27, 2014


Approximate Gaussian process inference for the drift function in stochastic differential equations
Andreas Ruttor, Philipp Batz and Manfred Opper
Advances in Neural Information Processing Systems 26, 2013


Approximate inference in latent Gaussian-Markov models from continuous time observations
Botond Cseke, Manfred Opper and Guido Sanguinetti
Advances in Neural Information Processing Systems 26, 2013


Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach
Florian Stimberg, Andreas Ruttor and Manfred Opper
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012


Inference in continuous-time change-point models
Florian Stimberg, Manfred Opper, Guido Sanguinetti and Andreas Ruttor
Advances in Neural Information Processing Systems 24, 2011


Analytical Results for the Error in Filtering of Gaussian Processes
Alex K. Susemihl, Ron Meir and Manfred Opper
Advances in Neural Information Processing Systems 24, 2011


Approximate inference in continuous time Gaussian-Jump processes
Manfred Opper, Andreas Ruttor and Guido Sanguinetti
Advances in Neural Information Processing Systems 23, 2010


Approximate parameter inference in a stochastic reaction-diffusion model
Andreas Ruttor and Manfred Opper
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Regret Bounds for Gaussian Process Bandit Problems
Steffen Grünewälder, Jean-yves Audibert, Manfred Opper and John Shawe-taylor
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Perturbation Corrections in Approximate Inference: Mixture Modelling Applications
Ulrich Paquet, Ole Winther and Manfred Opper
Journal of Machine Learning Research, 2009


Improving on Expectation Propagation
Manfred Opper, Ulrich Paquet and Ole Winther
Advances in Neural Information Processing Systems 21, 2008


Variational inference for Markov jump processes
Manfred Opper and Guido Sanguinetti
Advances in Neural Information Processing Systems 20, 2007


Variational Inference for Diffusion Processes
Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford and John S. Shawe-taylor
Advances in Neural Information Processing Systems 20, 2007


Expectation Consistent Approximate Inference
Manfred Opper and Ole Winther
Journal of Machine Learning Research, 2005


An Approximate Inference Approach for the PCA Reconstruction Error
Manfred Opper
Advances in Neural Information Processing Systems 18, 2005


Expectation Consistent Free Energies for Approximate Inference
Manfred Opper and Ole Winther
Advances in Neural Information Processing Systems 17, 2004


Approximate Analytical Bootstrap Averages for Support Vector Classifiers
Dörthe Malzahn and Manfred Opper
Advances in Neural Information Processing Systems 16, 2003


Variational Linear Response
Manfred Opper and Ole Winther
Advances in Neural Information Processing Systems 16, 2003


An Approximate Analytical Approach to Resampling Averages
Dörthe Malzahn and Manfred Opper
Journal of Machine Learning Research, 2003


A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages
Dörthe Malzahn and Manfred Opper
Advances in Neural Information Processing Systems 15, 2002


A Variational Approach to Learning Curves
Dörthe Malzahn and Manfred Opper
Advances in Neural Information Processing Systems 14, 2001


Asymptotic Universality for Learning Curves of Support Vector Machines
Manfred Opper and Robert Urbanczik
Advances in Neural Information Processing Systems 14, 2001


TAP Gibbs Free Energy, Belief Propagation and Sparsity
Lehel Csató, Manfred Opper and Ole Winther
Advances in Neural Information Processing Systems 14, 2001