Manfred Opper

ORCID

Publication Activity (10 Years)

Years Active: 1991-2024
Publications (10 Years): 43

Top Venues

Publications

Ludwig Winkler, Lorenz Richter, Manfred Opper
Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models. CoRR (2024)
Burak Çakmak, Yue M. Lu, Manfred Opper
A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification. CoRR (2024)
Rembert Daems, Manfred Opper, Guillaume Crevecoeur, Tolga Birdal
Variational Inference for SDEs Driven by Fractional Noise. ICLR (2024)
Rembert Daems, Manfred Opper, Guillaume Crevecoeur, Tolga Birdal
Variational Inference for SDEs Driven by Fractional Noise. CoRR (2023)
Ludwig Winkler, César Ojeda, Manfred Opper
A Score-Based Approach for Training Schrödinger Bridges for Data Modelling. Entropy 25 (2) (2023)
Burak Çakmak, Eleni Gkiouzepi, Manfred Opper, Giuseppe Caire
Inference in Linear Observations with Multiple Signal Sources: Analysis of Approximate Message Passing and Applications to Unsourced Random Access in Cell-Free Systems. CoRR (2023)
Burak Çakmak, Yue M. Lu, Manfred Opper
Analysis of Random Sequential Message Passing Algorithms for Approximate Inference. CoRR (2022)
Noa Malem-Shinitski, César Ojeda, Manfred Opper
Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects. Entropy 24 (3) (2022)
Ludwig Winkler, César Ojeda, Manfred Opper
Stochastic Control for Bayesian Neural Network Training. Entropy 24 (8) (2022)
Christian Molkenthin, Christian Donner, Sebastian Reich, Gert Zöller, Sebastian Hainzl, Matthias Holschneider, Manfred Opper
GP-ETAS: semiparametric Bayesian inference for the spatio-temporal epidemic type aftershock sequence model. Stat. Comput. 32 (2) (2022)
Théo Galy-Fajou, Manfred Opper
Adaptive Inducing Points Selection For Gaussian Processes. CoRR (2021)
Burak Çakmak, Manfred Opper
Exact solution to the random sequential dynamics of a message passing algorithm. CoRR (2021)
Théo Galy-Fajou, Valerio Perrone, Manfred Opper
Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation. Entropy 23 (8) (2021)
Noa Malem-Shinitski, Cesar Ojeda, Manfred Opper
Nonlinear Hawkes Process with Gaussian Process Self Effects. CoRR (2021)
Manfred Opper, Burak Çakmak
Understanding the dynamics of message passing algorithms: a free probability heuristics. CoRR (2020)
Théo Galy-Fajou, Florian Wenzel, Manfred Opper
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. CoRR (2020)
Dimitra Maoutsa, Sebastian Reich, Manfred Opper
Interacting Particle Solutions of Fokker-Planck Equations Through Gradient-Log-Density Estimation. Entropy 22 (8) (2020)
Burak Çakmak, Manfred Opper
A Dynamical Mean-Field Theory for Learning in Restricted Boltzmann Machines. CoRR (2020)
Noa Malem-Shinitski, Manfred Opper, Sebastian Reich, Lisa Schwetlick, Stefan A. Seelig, Ralf Engbert
A mathematical model of local and global attention in natural scene viewing. PLoS Comput. Biol. 16 (12) (2020)
Burak Çakmak, Manfred Opper
Analysis of Bayesian Inference Algorithms by the Dynamical Functional Approach. CoRR (2020)
Théo Galy-Fajou, Florian Wenzel, Manfred Opper
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. AISTATS (2020)
Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper
Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation. AAAI (2019)
Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation. UAI (2019)
Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann
Statistical physics of learning and inference. ESANN (2019)
Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory. CoRR (2019)
Burak Çakmak, Manfred Opper
Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices. CoRR (2019)
Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation. CoRR (2019)
Burak Çakmak, Manfred Opper
Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices. ISIT (2019)
Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation. CoRR (2018)
Yuval Harel, Ron Meir, Manfred Opper
Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form Approximation. Neural Comput. 30 (8) (2018)
Christian Donner, Manfred Opper
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes. J. Mach. Learn. Res. 19 (2018)
Christian Donner, Manfred Opper
Efficient Bayesian Inference for a Gaussian Process Density Model. CoRR (2018)
Burak Çakmak, Manfred Opper
Expectation Propagation for Approximate Inference: Free Probability Framework. CoRR (2018)
Christian Donner, Manfred Opper
Efficient Bayesian Inference for a Gaussian Process Density Model. UAI (2018)
Christian Donner, Manfred Opper
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes. CoRR (2018)
Burak Çakmak, Manfred Opper
Expectation Propagation for Approximate Inference: Free Probability Framework. ISIT (2018)
Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
Perturbative Black Box Variational Inference. CoRR (2017)
Manfred Opper
An estimator for the relative entropy rate of path measures for stochastic differential equations. J. Comput. Phys. 330 (2017)
Burak Çakmak, Manfred Opper, Ole Winther, Bernard H. Fleury
Dynamical Functional Theory for Compressed Sensing. CoRR (2017)
Burak Çakmak, Manfred Opper, Ole Winther, Bernard H. Fleury
Dynamical functional theory for compressed sensing. ISIT (2017)
Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
Perturbative Black Box Variational Inference. NIPS (2017)
Gina Gruenhage, Manfred Opper, Simon Barthelmé
Visualizing the effects of a changing distance on data using continuous embeddings. Comput. Stat. Data Anal. 104 (2016)
Burak Çakmak, Manfred Opper, Bernard H. Fleury, Ole Winther
Self-Averaging Expectation Propagation. CoRR (2016)
Manfred Opper, Burak Çakmak, Ole Winther
A Theory of Solving TAP Equations for Ising Models with General Invariant Random Matrices. CoRR (2015)
Yuval Harel, Ron Meir, Manfred Opper
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding. NIPS (2015)
Alex K. Susemihl, Ron Meir, Manfred Opper
Optimal Neural Codes for Control and Estimation. NIPS (2014)
Florian Stimberg, Andreas Ruttor, Manfred Opper
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data. NIPS (2014)
Alex K. Susemihl, Ron Meir, Manfred Opper
Optimal Population Codes for Control and Estimation. CoRR (2014)
Chris Häusler, Alex K. Susemihl, Martin P. Nawrot, Manfred Opper
Temporal Autoencoding Improves Generative Models of Time Series. CoRR (2013)
Sven Wiethölter, Andreas Ruttor, Uwe Bergemann, Manfred Opper, Adam Wolisz
DARA: Estimating the behavior of data rate adaptation algorithms in WLAN hotspots. INFOCOM (2013)
Botond Cseke, Manfred Opper, Guido Sanguinetti
Approximate inference in latent Gaussian-Markov models from continuous time observations. NIPS (2013)
Andreas Ruttor, Philipp Batz, Manfred Opper
Approximate Gaussian process inference for the drift function in stochastic differential equations. NIPS (2013)
Manfred Opper, Ulrich Paquet, Ole Winther
Perturbative corrections for approximate inference in Gaussian latent variable models. J. Mach. Learn. Res. 14 (1) (2013)
Hilbert J. Kappen, Vicenç Gómez, Manfred Opper
Optimal Control as a Graphical Model Inference Problem. ICAPS (2013)
Yuan Shen, Dan Cornford, Manfred Opper, Cédric Archambeau
Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions. Comput. Stat. 27 (1) (2012)
Hilbert J. Kappen, Vicenç Gómez, Manfred Opper
Optimal control as a graphical model inference problem. Mach. Learn. 87 (2) (2012)
Florian Stimberg, Andreas Ruttor, Manfred Opper
Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach. AISTATS (2012)
Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor
Inference in continuous-time change-point models. NIPS (2011)
Fabiano L. Ribeiro, Manfred Opper
Expectation Propagation with Factorizing Distributions: A Gaussian Approximation and Performance Results for Simple Models. Neural Comput. 23 (4) (2011)
Alex K. Susemihl, Ron Meir, Manfred Opper
Analytical Results for the Error in Filtering of Gaussian Processes. NIPS (2011)
Steffen Grünewälder, Jean-Yves Audibert, Manfred Opper, John Shawe-Taylor
Regret Bounds for Gaussian Process Bandit Problems. AISTATS (2010)
Yuan Shen, Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor, Remi Louis Barillec
A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems. J. Signal Process. Syst. 61 (1) (2010)
Andreas Ruttor, Manfred Opper
Approximate parameter inference in a stochastic reaction-diffusion model. AISTATS (2010)
Michael Dewar, Visakan Kadirkamanathan, Manfred Opper, Guido Sanguinetti
Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster. BMC Syst. Biol. 4 (2010)
Manfred Opper, Andreas Ruttor, Guido Sanguinetti
Approximate inference in continuous time Gaussian-Jump processes. NIPS (2010)
Manfred Opper, Guido Sanguinetti
Learning combinatorial transcriptional dynamics from gene expression data. Bioinform. 26 (13) (2010)
Michail D. Vrettas, Dan Cornford, Manfred Opper, Yuan Shen
A new variational radial basis function approximation for inference in multivariate diffusions. Neurocomputing 73 (7-9) (2010)
Guido Sanguinetti, Andreas Ruttor, Manfred Opper, Cédric Archambeau
Switching regulatory models of cellular stress response. Bioinform. 25 (10) (2009)
Manfred Opper, Cédric Archambeau
The Variational Gaussian Approximation Revisited. Neural Comput. 21 (3) (2009)
Bert Kappen, Vicenç Gómez, Manfred Opper
Optimal control as a graphical model inference problem CoRR (2009)
Ulrich Paquet, Ole Winther, Manfred Opper
Perturbation Corrections in Approximate Inference: Mixture Modelling Applications. J. Mach. Learn. Res. 10 (2009)
Manfred Opper, Ulrich Paquet, Ole Winther
Improving on Expectation Propagation. NIPS (2008)
Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor
Gaussian Process Approximations of Stochastic Differential Equations. Gaussian Processes in Practice (2007)
Manfred Opper, Guido Sanguinetti
Variational inference for Markov jump processes. NIPS (2007)
Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor
Variational Inference for Diffusion Processes. NIPS (2007)
Manfred Opper
An Approximate Inference Approach for the PCA Reconstruction Error. NIPS (2005)
Manfred Opper, Ole Winther
Expectation Consistent Approximate Inference. J. Mach. Learn. Res. 6 (2005)
Manfred Opper, Ole Winther
Expectation Consistent Free Energies for Approximate Inference. NIPS (2004)
Manfred Opper, Ole Winther
Approximate Inference in Probabilistic Models. ALT (2004)
Dörthe Malzahn, Manfred Opper
Learning curves and bootstrap estimates for inference with Gaussian processes: A statistical mechanics study. Complex. 8 (4) (2003)
Dörthe Malzahn, Manfred Opper
An Approximate Analytical Approach to Resampling Averages. J. Mach. Learn. Res. 4 (2003)
Manfred Opper, Ole Winther
Variational Linear Response. NIPS (2003)
Dörthe Malzahn, Manfred Opper
Approximate Analytical Bootstrap Averages for Support Vector Classifiers. NIPS (2003)
Lehel Csató, Manfred Opper, Ole Winther
Tractable inference for probabilistic data models. Complex. 8 (4) (2003)
Yoav Freund, Manfred Opper
Drifting Games and Brownian Motion. J. Comput. Syst. Sci. 64 (1) (2002)
Robert D. Stewart, Iris Fermin, Manfred Opper
Region growing with pulse-coupled neural networks: an alternative to seeded region growing. IEEE Trans. Neural Networks 13 (6) (2002)
Lehel Csató, Manfred Opper
Sparse On-Line Gaussian Processes. Neural Comput. 14 (3) (2002)
Dörthe Malzahn, Manfred Opper
A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages. NIPS (2002)
Dörthe Malzahn, Manfred Opper
A Variational Approach to Learning Curves. NIPS (2001)
Manfred Opper, Robert Urbanczik
Asymptotic Universality for Learning Curves of Support Vector Machines. NIPS (2001)
Lehel Csató, Dan Cornford, Manfred Opper
Online Approximations for Wind-Field Models. ICANN (2001)
Dörthe Malzahn, Manfred Opper
Learning Curves for Gaussian Processes Models: Fluctuations and Universality. ICANN (2001)
Lehel Csató, Manfred Opper, Ole Winther
TAP Gibbs Free Energy, Belief Propagation and Sparsity. NIPS (2001)
Yoav Freund, Manfred Opper
Continuous Drifting Games. COLT (2000)
Dörthe Malzahn, Manfred Opper
Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations. NIPS (2000)
Lehel Csató, Manfred Opper
Sparse Representation for Gaussian Process Models. NIPS (2000)
Manfred Opper, Ole Winther
Gaussian Processes for Classification: Mean-Field Algorithms. Neural Comput. 12 (11) (2000)
Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther
Efficient Approaches to Gaussian Process Classification. NIPS (1999)
Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper
Finite-Dimensional Approximation of Gaussian Processes. NIPS (1998)
Manfred Opper, Francesco Vivarelli
General Bounds on Bayes Errors for Regression with Gaussian Processes. NIPS (1998)

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Publication Activity (10 Years)

Top Topics

Top Venues

Manfred Opper