Manfred Opper
51 publications
11 venues
H Index 20
Affiliation
Technical University of Berlin, Department of Mathematics, GermanyLinks
| Name | Venue | Year | citations |
|---|---|---|---|
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models.
|
AISTATS | 2020 | 3 |
| Statistical physics of learning and inference.
|
ESANN | 2019 | 3 |
| Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation.
|
UAI | 2019 | 20 |
| Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation.
|
AAAI | 2019 | 0 |
| Efficient Bayesian Inference for a Gaussian Process Density Model.
|
UAI | 2018 | 11 |
| Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes.
|
JMLR | 2018 | 23 |
| Perturbative Black Box Variational Inference.
|
NIPS/NeurIPS | 2017 | 35 |
| A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding.
|
NIPS/NeurIPS | 2015 | 10 |
| Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data.
|
NIPS/NeurIPS | 2014 | 3 |
| Optimal Neural Codes for Control and Estimation.
|
NIPS/NeurIPS | 2014 | 5 |
| Perturbative corrections for approximate inference in Gaussian latent variable models.
|
JMLR | 2013 | 14 |
| Approximate inference in latent Gaussian-Markov models from continuous time observations.
|
NIPS/NeurIPS | 2013 | 16 |
| Approximate Gaussian process inference for the drift function in stochastic differential equations.
|
NIPS/NeurIPS | 2013 | 39 |
| Optimal Control as a Graphical Model Inference Problem.
|
ICAPS | 2013 | 0 |
| Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach.
|
AISTATS | 2012 | 12 |
| Optimal control as a graphical model inference problem.
|
MLJ | 2012 | 1 |
| Analytical Results for the Error in Filtering of Gaussian Processes.
|
NIPS/NeurIPS | 2011 | 9 |
| Inference in continuous-time change-point models.
|
NIPS/NeurIPS | 2011 | 14 |
| Approximate parameter inference in a stochastic reaction-diffusion model.
|
AISTATS | 2010 | 8 |
| Approximate inference in continuous time Gaussian-Jump processes.
|
NIPS/NeurIPS | 2010 | 7 |
| Regret Bounds for Gaussian Process Bandit Problems.
|
AISTATS | 2010 | 87 |
| Perturbation Corrections in Approximate Inference: Mixture Modelling Applications.
|
JMLR | 2009 | 21 |
| Improving on Expectation Propagation.
|
NIPS/NeurIPS | 2008 | 20 |
| Variational inference for Markov jump processes.
|
NIPS/NeurIPS | 2007 | 88 |
| Variational Inference for Diffusion Processes.
|
NIPS/NeurIPS | 2007 | 89 |
| Expectation Consistent Approximate Inference.
|
JMLR | 2005 | 216 |
| An Approximate Inference Approach for the PCA Reconstruction Error.
|
NIPS/NeurIPS | 2005 | 4 |
| Expectation Consistent Free Energies for Approximate Inference.
|
NIPS/NeurIPS | 2004 | 24 |
| Variational Linear Response.
|
NIPS/NeurIPS | 2003 | 12 |
| An Approximate Analytical Approach to Resampling Averages.
|
JMLR | 2003 | 10 |
| Approximate Analytical Bootstrap Averages for Support Vector Classifiers.
|
NIPS/NeurIPS | 2003 | 7 |
| A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages.
|
NIPS/NeurIPS | 2002 | 8 |
| Region growing with pulse-coupled neural networks: an alternative to seeded region growing.
|
IEEE Trans. Neural Networks | 2002 | 97 |
| Online Approximations for Wind-Field Models.
|
ICANN | 2001 | 6 |
| TAP Gibbs Free Energy, Belief Propagation and Sparsity.
|
NIPS/NeurIPS | 2001 | 36 |
| Asymptotic Universality for Learning Curves of Support Vector Machines.
|
NIPS/NeurIPS | 2001 | 0 |
| Learning Curves for Gaussian Processes Models: Fluctuations and Universality.
|
ICANN | 2001 | 7 |
| A Variational Approach to Learning Curves.
|
NIPS/NeurIPS | 2001 | 20 |
| Sparse Representation for Gaussian Process Models.
|
NIPS/NeurIPS | 2000 | 144 |
| Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations.
|
NIPS/NeurIPS | 2000 | 20 |
| Continuous Drifting Games.
|
COLT | 2000 | 4 |
| Efficient Approaches to Gaussian Process Classification.
|
NIPS/NeurIPS | 1999 | 66 |
| Finite-Dimensional Approximation of Gaussian Processes.
|
NIPS/NeurIPS | 1998 | 48 |
| General Bounds on Bayes Errors for Regression with Gaussian Processes.
|
NIPS/NeurIPS | 1998 | 49 |
| Mean Field Methods for Classification with Gaussian Processes.
|
NIPS/NeurIPS | 1998 | 22 |
| Dynamics of Training.
|
NIPS/NeurIPS | 1996 | 12 |
| A Mean Field Algorithm for Bayes Learning in Large Feed-forward Neural Networks.
|
NIPS/NeurIPS | 1996 | 15 |
| General Bounds on the Mutual Information Between a Parameter and
|
COLT | 1995 | 0 |
| Query by Committee.
|
COLT | 1992 | 0 |
| Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods.
|
NIPS/NeurIPS | 1991 | 14 |
| Calculation of the Learning Curve of Bayes Optimal Classification Algorithm for Learning a Perceptron With Noise.
|
COLT | 1991 | 66 |