Opper, Manfred

56 publications

ICLRW 2025 Fractional Brownian Bridges for Aligned Data Gabriel Nobis, Arina Belova, Maximilian Springenberg, Rembert Daems, Christoph Knochenhauer, Manfred Opper, Tolga Birdal, Wojciech Samek

NeurIPS 2025 Fractional Diffusion Bridge Models Gabriel Nobis, Maximilian Springenberg, Arina Belova, Rembert Daems, Christoph Knochenhauer, Manfred Opper, Tolga Birdal, Wojciech Samek

ICML 2024 Bridging Discrete and Continuous State Spaces: Exploring the Ehrenfest Process in Time-Continuous Diffusion Models Ludwig Winkler, Lorenz Richter, Manfred Opper

NeurIPS 2024 Generative Fractional Diffusion Models Gabriel Nobis, Maximilian Springenberg, Marco Aversa, Michael Detzel, Rembert Daems, Roderick Murray-Smith, Shinichi Nakajima, Sebastian Lapuschkin, Stefano Ermon, Tolga Birdal, Manfred Opper, Christoph Knochenhauer, Luis Oala, Wojciech Samek

ICMLW 2024 Generative Fractional Diffusion Models Gabriel Nobis, Maximilian Springenberg, Marco Aversa, Michael Detzel, Rembert Daems, Roderick Murray-Smith, Shinichi Nakajima, Sebastian Lapuschkin, Stefano Ermon, Tolga Birdal, Manfred Opper, Christoph Knochenhauer, Luis Oala, Wojciech Samek

ICLR 2024 Variational Inference for SDEs Driven by Fractional Noise Rembert Daems, Manfred Opper, Guillaume Crevecoeur, Tolga Birdal

NeurIPSW 2023 Variational Inference for SDEs Driven by Fractional Noise Rembert Daems, Manfred Opper, Guillaume Crevecoeur, Tolga Birdal

AISTATS 2020 Automated Augmented Conjugate Inference for Non-Conjugate Gaussian Process Models Theo Galy-Fajou, Florian Wenzel, Manfred Opper

AAAI 2019 Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper

UAI 2019 Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper

UAI 2018 Efficient Bayesian Inference for a Gaussian Process Density Model Christian Donner, Manfred Opper

JMLR 2018 Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes Christian Donner, Manfred Opper

NeurIPS 2017 Perturbative Black Box Variational Inference Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt

NeurIPS 2015 A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding Yuval Harel, Ron Meir, Manfred Opper

NeurIPS 2014 Optimal Neural Codes for Control and Estimation Alex K. Susemihl, Ron Meir, Manfred Opper

NeurIPS 2014 Poisson Process Jumping Between an Unknown Number of Rates: Application to Neural Spike Data Florian Stimberg, Andreas Ruttor, Manfred Opper

NeurIPS 2013 Approximate Gaussian Process Inference for the Drift Function in Stochastic Differential Equations Andreas Ruttor, Philipp Batz, Manfred Opper

NeurIPS 2013 Approximate Inference in Latent Gaussian-Markov Models from Continuous Time Observations Botond Cseke, Manfred Opper, Guido Sanguinetti

JMLR 2013 Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models Manfred Opper, Ulrich Paquet, Ole Winther

AISTATS 2012 Bayesian Inference for Change Points in Dynamical Systems with Reusable States - A Chinese Restaurant Process Approach Florian Stimberg, Andreas Ruttor, Manfred Opper

MLJ 2012 Optimal Control as a Graphical Model Inference Problem Hilbert J. Kappen, Vicenç Gómez, Manfred Opper

NeurIPS 2011 Analytical Results for the Error in Filtering of Gaussian Processes Alex K. Susemihl, Ron Meir, Manfred Opper

NeurIPS 2011 Inference in Continuous-Time Change-Point Models Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor

NeurIPS 2010 Approximate Inference in Continuous Time Gaussian-Jump Processes Manfred Opper, Andreas Ruttor, Guido Sanguinetti

AISTATS 2010 Approximate Parameter Inference in a Stochastic Reaction-Diffusion Model Andreas Ruttor, Manfred Opper

AISTATS 2010 Regret Bounds for Gaussian Process Bandit Problems Steffen Grünewälder, Jean–Yves Audibert, Manfred Opper, John Shawe–Taylor

JMLR 2009 Perturbation Corrections in Approximate Inference: Mixture Modelling Applications Ulrich Paquet, Ole Winther, Manfred Opper

NeurIPS 2008 Improving on Expectation Propagation Manfred Opper, Ulrich Paquet, Ole Winther

NeurIPS 2007 Variational Inference for Diffusion Processes Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John S. Shawe-taylor

NeurIPS 2007 Variational Inference for Markov Jump Processes Manfred Opper, Guido Sanguinetti

NeurIPS 2005 An Approximate Inference Approach for the PCA Reconstruction Error Manfred Opper

JMLR 2005 Expectation Consistent Approximate Inference Manfred Opper, Ole Winther

ALT 2004 Approximate Inference in Probabilistic Models Manfred Opper, Ole Winther

NeurIPS 2004 Expectation Consistent Free Energies for Approximate Inference Manfred Opper, Ole Winther

JMLR 2003 An Approximate Analytical Approach to Resampling Averages (Kernel Machines Section) Dörthe Malzahn, Manfred Opper

NeurIPS 2003 Approximate Analytical Bootstrap Averages for Support Vector Classifiers Dörthe Malzahn, Manfred Opper

NeurIPS 2003 Variational Linear Response Manfred Opper, Ole Winther

NeurIPS 2002 A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages Dörthe Malzahn, Manfred Opper

NeCo 2002 Sparse On-Line Gaussian Processes Lehel Csató, Manfred Opper

NeurIPS 2001 A Variational Approach to Learning Curves Dörthe Malzahn, Manfred Opper

NeurIPS 2001 Asymptotic Universality for Learning Curves of Support Vector Machines Manfred Opper, Robert Urbanczik

NeurIPS 2001 TAP Gibbs Free Energy, Belief Propagation and Sparsity Lehel Csató, Manfred Opper, Ole Winther

COLT 2000 Continuous Drifting Games Yoav Freund, Manfred Opper

NeCo 2000 Gaussian Processes for Classification: Mean-Field Algorithms Manfred Opper, Ole Winther

NeurIPS 2000 Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations Dörthe Malzahn, Manfred Opper

NeurIPS 2000 Sparse Representation for Gaussian Process Models Lehel Csató, Manfred Opper

NeurIPS 1999 Efficient Approaches to Gaussian Process Classification Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther

NeurIPS 1998 Finite-Dimensional Approximation of Gaussian Processes Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper

NeurIPS 1998 General Bounds on Bayes Errors for Regression with Gaussian Processes Manfred Opper, Francesco Vivarelli

NeurIPS 1998 Mean Field Methods for Classification with Gaussian Processes Manfred Opper, Ole Winther

NeurIPS 1996 A Mean Field Algorithm for Bayes Learning in Large Feed-Forward Neural Networks Manfred Opper, Ole Winther

NeurIPS 1996 Dynamics of Training Siegfried Bös, Manfred Opper

COLT 1995 General Bounds on the Mutual Information Between a Parameter and N Conditionally Independent Observations David Haussler, Manfred Opper

COLT 1992 Query by Committee H. Sebastian Seung, Manfred Opper, Haim Sompolinsky

COLT 1991 Calculation of the Learning Curve of Bayes Optimal Classification Algorithm for Learning a Perceptron with Noise Manfred Opper, David Haussler

NeurIPS 1991 Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods David Haussler, Michael Kearns, Manfred Opper, Robert Schapire