Calculation of the Learning Curve of Bayes Optimal Classification Algorithm for Learning a Perceptron With Noise. - Manfred Opper; David Haussler (COLT, 1991)

Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. - David Haussler; Michael J. Kearns; Manfred Opper; Robert E. Schapire (NIPS, 1991)

Query by Committee. - H. Sebastian Seung; Manfred Opper; Haim Sompolinsky (COLT, 1992)

General Bounds on the Mutual Information Between a Parameter and n Conditionally Independent Observations. - David Haussler; Manfred Opper (COLT, 1995)

Dynamics of Training. - Siegfried Bös; Manfred Opper (NIPS, 1996)

A Mean Field Algorithm for Bayes Learning in Large Feed-forward Neural Networks. - Manfred Opper; Ole Winther (NIPS, 1996)

Metric Entropy and Minimax Risk in Classification. - David Haussler; Manfred Opper (Structures in Logic and Computer Science, 1997)

Finite-Dimensional Approximation of Gaussian Processes. - Giancarlo Ferrari-Trecate; Christopher K. I. Williams; Manfred Opper (NIPS, 1998)

General Bounds on Bayes Errors for Regression with Gaussian Processes. - Manfred Opper; Francesco Vivarelli (NIPS, 1998)

Mean Field Methods for Classification with Gaussian Processes. - Manfred Opper; Ole Winther (NIPS, 1998)

Efficient Approaches to Gaussian Process Classification. - Lehel Csató; Ernest Fokoué; Manfred Opper; Bernhard Schottky; Ole Winther (NIPS, 1999)

Continuous Drifting Games. - Yoav Freund; Manfred Opper (COLT, 2000)

Sparse Representation for Gaussian Process Models. - Lehel Csató; Manfred Opper (NIPS, 2000)

Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations. - Dörthe Malzahn; Manfred Opper (NIPS, 2000)

Gaussian Processes for Classification: Mean-Field Algorithms. - Manfred Opper; Ole Winther (Neural Comput., 2000)

Online Approximations for Wind-Field Models. - Lehel Csató; Dan Cornford; Manfred Opper (ICANN, 2001)

Learning Curves for Gaussian Processes Models: Fluctuations and Universality. - Dörthe Malzahn; Manfred Opper (ICANN, 2001)

TAP Gibbs Free Energy, Belief Propagation and Sparsity. - Lehel Csató; Manfred Opper; Ole Winther (NIPS, 2001)

A Variational Approach to Learning Curves. - Dörthe Malzahn; Manfred Opper (NIPS, 2001)

Asymptotic Universality for Learning Curves of Support Vector Machines. - Manfred Opper; Robert Urbanczik (NIPS, 2001)

A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages. - Dörthe Malzahn; Manfred Opper (NIPS, 2002)

Drifting Games and Brownian Motion. - Yoav Freund; Manfred Opper (J. Comput. Syst. Sci., 2002)

Sparse On-Line Gaussian Processes. - Lehel Csató; Manfred Opper (Neural Comput., 2002)

Region growing with pulse-coupled neural networks: an alternative to seeded region growing. - Robert D. Stewart; Iris Fermin; Manfred Opper (IEEE Trans. Neural Networks, 2002)

Approximate Analytical Bootstrap Averages for Support Vector Classifiers. - Dörthe Malzahn; Manfred Opper (NIPS, 2003)

Variational Linear Response. - Manfred Opper; Ole Winther (NIPS, 2003)

Tractable inference for probabilistic data models. - Lehel Csató; Manfred Opper; Ole Winther (Complex., 2003)

Learning curves and bootstrap estimates for inference with Gaussian processes: A statistical mechanics study. - Dörthe Malzahn; Manfred Opper (Complex., 2003)

An Approximate Analytical Approach to Resampling Averages. - Dörthe Malzahn; Manfred Opper (J. Mach. Learn. Res., 2003)

Approximate Inference in Probabilistic Models. - Manfred Opper; Ole Winther (ALT, 2004)

Expectation Consistent Free Energies for Approximate Inference. - Manfred Opper; Ole Winther (NIPS, 2004)

An Approximate Inference Approach for the PCA Reconstruction Error. - Manfred Opper (NIPS, 2005)

Expectation Consistent Approximate Inference. - Manfred Opper; Ole Winther (J. Mach. Learn. Res., 2005)

Variational Inference for Diffusion Processes. - Cédric Archambeau; Manfred Opper; Yuan Shen 001; Dan Cornford; John Shawe-Taylor (NIPS, 2007)

Variational inference for Markov jump processes. - Manfred Opper; Guido Sanguinetti (NIPS, 2007)

Gaussian Process Approximations of Stochastic Differential Equations. - Cédric Archambeau; Dan Cornford; Manfred Opper; John Shawe-Taylor (Gaussian Processes in Practice, 2007)

Improving on Expectation Propagation. - Manfred Opper; Ulrich Paquet; Ole Winther (NIPS, 2008)

Switching regulatory models of cellular stress response. - Guido Sanguinetti; Andreas Ruttor; Manfred Opper; Cédric Archambeau (Bioinform., 2009)

Perturbation Corrections in Approximate Inference: Mixture Modelling Applications. - Ulrich Paquet; Ole Winther; Manfred Opper (J. Mach. Learn. Res., 2009)

The Variational Gaussian Approximation Revisited. - Manfred Opper; Cédric Archambeau (Neural Comput., 2009)

Approximate Inference for Stochastic Reaction processes. - Andreas Ruttor; Guido Sanguinetti; Manfred Opper (Learning and Inference in Computational Systems Biology, 2010)

Approximate inference in continuous time Gaussian-Jump processes. - Manfred Opper; Andreas Ruttor; Guido Sanguinetti (NIPS, 2010)

Learning combinatorial transcriptional dynamics from gene expression data. - Manfred Opper; Guido Sanguinetti (Bioinform., 2010)

Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster. - Michael Dewar; Visakan Kadirkamanathan; Manfred Opper; Guido Sanguinetti (BMC Syst. Biol., 2010)

A new variational radial basis function approximation for inference in multivariate diffusions. - Michail D. Vrettas; Dan Cornford; Manfred Opper; Yuan Shen 001 (Neurocomputing, 2010)

Regret Bounds for Gaussian Process Bandit Problems. - Steffen Grünewälder; Jean-Yves Audibert; Manfred Opper; John Shawe-Taylor (AISTATS, 2010)

Approximate parameter inference in a stochastic reaction-diffusion model. - Andreas Ruttor; Manfred Opper (AISTATS, 2010)

A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems. - Yuan Shen 001; Cédric Archambeau; Dan Cornford; Manfred Opper; John Shawe-Taylor; Remi Louis Barillec (J. Signal Process. Syst., 2010)

Inference in continuous-time change-point models. - Florian Stimberg; Manfred Opper; Guido Sanguinetti; Andreas Ruttor (NIPS, 2011)

Analytical Results for the Error in Filtering of Gaussian Processes. - Alex K. Susemihl; Ron Meir; Manfred Opper (NIPS, 2011)

Expectation Propagation with Factorizing Distributions: A Gaussian Approximation and Performance Results for Simple Models. - Fabiano L. Ribeiro; Manfred Opper (Neural Comput., 2011)

Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions. - Yuan Shen 001; Dan Cornford; Manfred Opper; Cédric Archambeau (Comput. Stat., 2012)

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

Optimal control as a graphical model inference problem. - Hilbert J. Kappen; Vicenç Gómez; Manfred Opper (Mach. Learn., 2012)

DARA: Estimating the behavior of data rate adaptation algorithms in WLAN hotspots. - Sven Wiethölter; Andreas Ruttor; Uwe Bergemann; Manfred Opper; Adam Wolisz (INFOCOM, 2013)

Approximate inference in latent Gaussian-Markov models from continuous time observations. - Botond Cseke; Manfred Opper; Guido Sanguinetti (NIPS, 2013)

Approximate Gaussian process inference for the drift function in stochastic differential equations. - Andreas Ruttor; Philipp Batz; Manfred Opper (NIPS, 2013)

Temporal Autoencoding Improves Generative Models of Time Series. - Chris Häusler; Alex K. Susemihl; Martin P. Nawrot; Manfred Opper (CoRR, 2013)

Perturbative corrections for approximate inference in Gaussian latent variable models. - Manfred Opper; Ulrich Paquet; Ole Winther (J. Mach. Learn. Res., 2013)

Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data. - Florian Stimberg; Andreas Ruttor; Manfred Opper (NIPS, 2014)

Optimal Neural Codes for Control and Estimation. - Alex K. Susemihl; Ron Meir; Manfred Opper (NIPS, 2014)

Optimal Population Codes for Control and Estimation. - Alex K. Susemihl; Ron Meir; Manfred Opper (CoRR, 2014)

A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding. - Yuval Harel; Ron Meir; Manfred Opper (NIPS, 2015)

A Theory of Solving TAP Equations for Ising Models with General Invariant Random Matrices. - Manfred Opper; Burak Çakmak; Ole Winther (CoRR, 2015)

Self-Averaging Expectation Propagation. - Burak Çakmak; Manfred Opper; Bernard H. Fleury; Ole Winther (CoRR, 2016)

Visualizing the effects of a changing distance on data using continuous embeddings. - Gina Gruenhage; Manfred Opper; Simon Barthelmé (Comput. Stat. Data Anal., 2016)

Dynamical functional theory for compressed sensing. - Burak Çakmak; Manfred Opper; Ole Winther; Bernard H. Fleury (ISIT, 2017)

Perturbative Black Box Variational Inference. - Robert Bamler; Cheng Zhang 005; Manfred Opper; Stephan Mandt (NIPS, 2017)

An estimator for the relative entropy rate of path measures for stochastic differential equations. - Manfred Opper (J. Comput. Phys., 2017)

Expectation Propagation for Approximate Inference: Free Probability Framework. - Burak Çakmak; Manfred Opper (ISIT, 2018)

Efficient Bayesian Inference for a Gaussian Process Density Model. - Christian Donner; Manfred Opper (UAI, 2018)

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes. - Christian Donner; Manfred Opper (J. Mach. Learn. Res., 2018)

Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form Approximation. - Yuval Harel; Ron Meir; Manfred Opper (Neural Comput., 2018)

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

Statistical physics of learning and inference. - Michael Biehl; Nestor Caticha; Manfred Opper; Thomas Villmann (ESANN, 2019)

Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices. - Burak Çakmak; Manfred Opper (ISIT, 2019)

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

Tightening Bounds for Variational Inference by Revisiting Perturbation Theory. - Robert Bamler; Cheng Zhang 005; Manfred Opper; Stephan Mandt (CoRR, 2019)

Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. - Théo Galy-Fajou; Florian Wenzel; Manfred Opper (AISTATS, 2020)

Analysis of Bayesian Inference Algorithms by the Dynamical Functional Approach. - Burak Çakmak; Manfred Opper (CoRR, 2020)

Understanding the dynamics of message passing algorithms: a free probability heuristics. - Manfred Opper; Burak Çakmak (CoRR, 2020)

A Dynamical Mean-Field Theory for Learning in Restricted Boltzmann Machines. - Burak Çakmak; Manfred Opper (CoRR, 2020)

Interacting Particle Solutions of Fokker-Planck Equations Through Gradient-Log-Density Estimation. - Dimitra Maoutsa; Sebastian Reich; Manfred Opper (Entropy, 2020)

A mathematical model of local and global attention in natural scene viewing. - Noa Malem-Shinitski; Manfred Opper; Sebastian Reich; Lisa Schwetlick; Stefan A. Seelig; Ralf Engbert (PLoS Comput. Biol., 2020)

Exact solution to the random sequential dynamics of a message passing algorithm. - Burak Çakmak; Manfred Opper (CoRR, 2021)

Nonlinear Hawkes Process with Gaussian Process Self Effects. - Noa Malem-Shinitski; César Ojeda; Manfred Opper (CoRR, 2021)

Adaptive Inducing Points Selection For Gaussian Processes. - Théo Galy-Fajou; Manfred Opper (CoRR, 2021)

Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation. - Théo Galy-Fajou; Valerio Perrone; Manfred Opper (Entropy, 2021)

Analysis of Random Sequential Message Passing Algorithms for Approximate Inference. - Burak Çakmak; Yue M. Lu; Manfred Opper (CoRR, 2022)

Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects. - Noa Malem-Shinitski; César Ojeda; Manfred Opper (Entropy, 2022)

Stochastic Control for Bayesian Neural Network Training. - Ludwig Winkler; César Ojeda; Manfred Opper (Entropy, 2022)

GP-ETAS: semiparametric Bayesian inference for the spatio-temporal epidemic type aftershock sequence model. - Christian Molkenthin; Christian Donner; Sebastian Reich; Gert Zöller; Sebastian Hainzl; Matthias Holschneider; Manfred Opper (Stat. Comput., 2022)

Inference in Linear Observations with Multiple Signal Sources: Analysis of Approximate Message Passing and Applications to Unsourced Random Access in Cell-Free Systems. - Burak Çakmak; Eleni Gkiouzepi; Manfred Opper; Giuseppe Caire (CoRR, 2023)

A Score-Based Approach for Training Schrödinger Bridges for Data Modelling. - Ludwig Winkler; César Ojeda; Manfred Opper (Entropy, 2023)

Variational Inference for SDEs Driven by Fractional Noise. - Rembert Daems; Manfred Opper; Guillaume Crevecoeur; Tolga Birdal (ICLR, 2024)

Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models. - Ludwig Winkler; Lorenz Richter; Manfred Opper (ICML, 2024)

A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification. - Burak Çakmak; Yue M. Lu; Manfred Opper (ISIT, 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; et al. (NeurIPS, 2024)

Joint Message Detection, Channel, and User Position Estimation for Unsourced Random Access in Cell-Free Networks. - Eleni Gkiouzepi; Burak Çakmak; Manfred Opper; Giuseppe Caire (SPAWC, 2024)

Efficient Training of Neural SDEs Using Stochastic Optimal Control. - Rembert Daems; Manfred Opper; Guillaume Crevecoeur; Tolga Birdal (CoRR, 2025)

Joint Message Detection and Channel Estimation for Unsourced Random Access in Cell-Free User-Centric Wireless Networks. - Burak Çakmak; Eleni Gkiouzepi; Manfred Opper; Giuseppe Caire (IEEE Trans. Inf. Theory, 2025)

On a mean-field Pontryagin minimum principle for stochastic optimal control. - Manfred Opper; Sebastian Reich (CoRR, 2025)