Manfred Opper - RankMe

120 papers A* 10A 6B 6C 2Journal 63Unranked 32

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Digital Twins: McKean-Pontryagin Control for Partially Observed Physical Twins. CoRR Manfred Opper, Sebastian Reich
2025	J	`jnl`	Efficient Training of Neural SDEs Using Stochastic Optimal Control. CoRR Rembert Daems, Manfred Opper, Guillaume Crevecoeur, Tolga Birdal
2025	J	`jnl`	Fractional Diffusion Bridge Models. CoRR Gabriel Nobis, Maximilian Springenberg, Arina Belova, Rembert Daems, Christoph Knochenhauer, Manfred Opper, Tolga Birdal, Wojciech Samek
2025	J	`jnl`	Joint Message Detection and Channel Estimation for Unsourced Random Access in Cell-Free User-Centric Wireless Networks. IEEE Trans. Inf. Theory Burak Çakmak, Eleni Gkiouzepi, Manfred Opper, Giuseppe Caire
2025	J	`jnl`	Multimodal Generative Flows for LHC Jets. CoRR Darius A. Faroughy, Manfred Opper, César Ojeda
2025	J	`jnl`	On a mean-field Pontryagin minimum principle for stochastic optimal control. CoRR Manfred Opper, Sebastian Reich
2024	B	`conf`	A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification. ISIT Burak Çakmak, Yue M. Lu, Manfred Opper
2024	J	`jnl`	A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification. CoRR Burak Çakmak, Yue M. Lu, Manfred Opper
2024	A*	`conf`	Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models. ICML Ludwig Winkler, Lorenz Richter, Manfred Opper
2024	J	`jnl`	Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models. CoRR Ludwig Winkler, Lorenz Richter, Manfred Opper
2024	A*	`conf`	Generative Fractional Diffusion Models. NeurIPS 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
2024	—	`conf`	Joint Message Detection, Channel, and User Position Estimation for Unsourced Random Access in Cell-Free Networks. SPAWC Eleni Gkiouzepi, Burak Çakmak, Manfred Opper, Giuseppe Caire
2024	J	`jnl`	Joint Message Detection, Channel, and User Position Estimation for Unsourced Random Access in Cell-Free Networks. CoRR Eleni Gkiouzepi, Burak Çakmak, Manfred Opper, Giuseppe Caire
2024	A*	`conf`	Variational Inference for SDEs Driven by Fractional Noise. ICLR Rembert Daems, Manfred Opper, Guillaume Crevecoeur, Tolga Birdal
2023	J	`jnl`	A Score-Based Approach for Training Schrödinger Bridges for Data Modelling. Entropy Ludwig Winkler, César Ojeda, Manfred Opper
2023	J	`jnl`	Inference in Linear Observations with Multiple Signal Sources: Analysis of Approximate Message Passing and Applications to Unsourced Random Access in Cell-Free Systems. CoRR Burak Çakmak, Eleni Gkiouzepi, Manfred Opper, Giuseppe Caire
2023	J	`jnl`	Variational Inference for SDEs Driven by Fractional Noise. CoRR Rembert Daems, Manfred Opper, Guillaume Crevecoeur, Tolga Birdal
2022	J	`jnl`	Analysis of Random Sequential Message Passing Algorithms for Approximate Inference. CoRR Burak Çakmak, Yue M. Lu, Manfred Opper
2022	J	`jnl`	GP-ETAS: semiparametric Bayesian inference for the spatio-temporal epidemic type aftershock sequence model. Stat. Comput. Christian Molkenthin, Christian Donner, Sebastian Reich, Gert Zöller, Sebastian Hainzl, Matthias Holschneider, Manfred Opper
2022	J	`jnl`	Stochastic Control for Bayesian Neural Network Training. Entropy Ludwig Winkler, César Ojeda, Manfred Opper
2022	J	`jnl`	Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects. Entropy Noa Malem-Shinitski, César Ojeda, Manfred Opper
2021	J	`jnl`	Adaptive Inducing Points Selection For Gaussian Processes. CoRR Théo Galy-Fajou, Manfred Opper
2021	J	`jnl`	Exact solution to the random sequential dynamics of a message passing algorithm. CoRR Burak Çakmak, Manfred Opper
2021	J	`jnl`	Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation. Entropy Théo Galy-Fajou, Valerio Perrone, Manfred Opper
2021	J	`jnl`	Nonlinear Hawkes Process with Gaussian Process Self Effects. CoRR Noa Malem-Shinitski, César Ojeda, Manfred Opper
2020	J	`jnl`	A Dynamical Mean-Field Theory for Learning in Restricted Boltzmann Machines. CoRR Burak Çakmak, Manfred Opper
2020	J	`jnl`	A mathematical model of local and global attention in natural scene viewing. PLoS Comput. Biol. Noa Malem-Shinitski, Manfred Opper, Sebastian Reich, Lisa Schwetlick, Stefan A. Seelig, Ralf Engbert
2020	J	`jnl`	Analysis of Bayesian Inference Algorithms by the Dynamical Functional Approach. CoRR Burak Çakmak, Manfred Opper
2020	A	`conf`	Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. AISTATS Théo Galy-Fajou, Florian Wenzel, Manfred Opper
2020	J	`jnl`	Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. CoRR Théo Galy-Fajou, Florian Wenzel, Manfred Opper
2020	J	`jnl`	Interacting Particle Solutions of Fokker-Planck Equations Through Gradient-Log-Density Estimation. Entropy Dimitra Maoutsa, Sebastian Reich, Manfred Opper
2020	J	`jnl`	Understanding the dynamics of message passing algorithms: a free probability heuristics. CoRR Manfred Opper, Burak Çakmak
2019	B	`conf`	Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices. ISIT Burak Çakmak, Manfred Opper
2019	J	`jnl`	Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices. CoRR Burak Çakmak, Manfred Opper
2019	A*	`conf`	Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation. AAAI Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper
2019	A	`conf`	Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation. UAI Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper
2019	J	`jnl`	Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation. CoRR Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper
2019	B	`conf`	Statistical physics of learning and inference. ESANN Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann
2019	J	`jnl`	Tightening Bounds for Variational Inference by Revisiting Perturbation Theory. CoRR Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
2018	A	`conf`	Efficient Bayesian Inference for a Gaussian Process Density Model. UAI Christian Donner, Manfred Opper
2018	J	`jnl`	Efficient Bayesian Inference for a Gaussian Process Density Model. CoRR Christian Donner, Manfred Opper
2018	J	`jnl`	Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes. CoRR Christian Donner, Manfred Opper
2018	J	`jnl`	Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes. J. Mach. Learn. Res. Christian Donner, Manfred Opper
2018	J	`jnl`	Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation. CoRR Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper
2018	B	`conf`	Expectation Propagation for Approximate Inference: Free Probability Framework. ISIT Burak Çakmak, Manfred Opper
2018	J	`jnl`	Expectation Propagation for Approximate Inference: Free Probability Framework. CoRR Burak Çakmak, Manfred Opper
2018	J	`jnl`	Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form Approximation. Neural Comput. Yuval Harel, Ron Meir, Manfred Opper
2017	J	`jnl`	An estimator for the relative entropy rate of path measures for stochastic differential equations. J. Comput. Phys. Manfred Opper
2017	J	`jnl`	Dynamical Functional Theory for Compressed Sensing. CoRR Burak Çakmak, Manfred Opper, Ole Winther, Bernard H. Fleury
2017	B	`conf`	Dynamical functional theory for compressed sensing. ISIT Burak Çakmak, Manfred Opper, Ole Winther, Bernard H. Fleury
2017	—	`conf`	Perturbative Black Box Variational Inference. NIPS Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
2017	J	`jnl`	Perturbative Black Box Variational Inference. CoRR Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
2016	J	`jnl`	Self-Averaging Expectation Propagation. CoRR Burak Çakmak, Manfred Opper, Bernard H. Fleury, Ole Winther
2016	J	`jnl`	Visualizing the effects of a changing distance on data using continuous embeddings. Comput. Stat. Data Anal. Gina Gruenhage, Manfred Opper, Simon Barthelmé
2015	J	`jnl`	A Theory of Solving TAP Equations for Ising Models with General Invariant Random Matrices. CoRR Manfred Opper, Burak Çakmak, Ole Winther
2015	—	`conf`	A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding. NIPS Yuval Harel, Ron Meir, Manfred Opper
2014	—	`conf`	Optimal Neural Codes for Control and Estimation. NIPS Alex K. Susemihl, Ron Meir, Manfred Opper
2014	J	`jnl`	Optimal Population Codes for Control and Estimation. CoRR Alex K. Susemihl, Ron Meir, Manfred Opper
2014	—	`conf`	Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data. NIPS Florian Stimberg, Andreas Ruttor, Manfred Opper
2013	—	`conf`	Approximate Gaussian process inference for the drift function in stochastic differential equations. NIPS Andreas Ruttor, Philipp Batz, Manfred Opper
2013	—	`conf`	Approximate inference in latent Gaussian-Markov models from continuous time observations. NIPS Botond Cseke, Manfred Opper, Guido Sanguinetti
2013	A*	`conf`	DARA: Estimating the behavior of data rate adaptation algorithms in WLAN hotspots. INFOCOM Sven Wiethölter, Andreas Ruttor, Uwe Bergemann, Manfred Opper, Adam Wolisz
2013	A*	`conf`	Optimal Control as a Graphical Model Inference Problem. ICAPS Hilbert J. Kappen, Vicenç Gómez, Manfred Opper
2013	J	`jnl`	Perturbative corrections for approximate inference in Gaussian latent variable models. J. Mach. Learn. Res. Manfred Opper, Ulrich Paquet, Ole Winther
2013	J	`jnl`	Temporal Autoencoding Improves Generative Models of Time Series. CoRR Chris Häusler, Alex K. Susemihl, Martin P. Nawrot, Manfred Opper
2012	A	`conf`	Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach. AISTATS Florian Stimberg, Andreas Ruttor, Manfred Opper
2012	J	`jnl`	Optimal control as a graphical model inference problem. Mach. Learn. Hilbert J. Kappen, Vicenç Gómez, Manfred Opper
2012	J	`jnl`	Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions. Comput. Stat. Yuan Shen, Dan Cornford, Manfred Opper, Cédric Archambeau
2011	—	`conf`	Analytical Results for the Error in Filtering of Gaussian Processes. NIPS Alex K. Susemihl, Ron Meir, Manfred Opper
2011	J	`jnl`	Expectation Propagation with Factorizing Distributions: A Gaussian Approximation and Performance Results for Simple Models. Neural Comput. Fabiano L. Ribeiro, Manfred Opper
2011	—	`conf`	Inference in continuous-time change-point models. NIPS Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor
2010	J	`jnl`	A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems. J. Signal Process. Syst. Yuan Shen, Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor, Remi Louis Barillec
2010	J	`jnl`	A new variational radial basis function approximation for inference in multivariate diffusions. Neurocomputing Michail D. Vrettas, Dan Cornford, Manfred Opper, Yuan Shen
2010	—	`ch.`	Approximate Inference for Stochastic Reaction processes. Learning and Inference in Computational Systems Biology Andreas Ruttor, Guido Sanguinetti, Manfred Opper
2010	—	`conf`	Approximate inference in continuous time Gaussian-Jump processes. NIPS Manfred Opper, Andreas Ruttor, Guido Sanguinetti
2010	A	`conf`	Approximate parameter inference in a stochastic reaction-diffusion model. AISTATS Andreas Ruttor, Manfred Opper
2010	J	`jnl`	Learning combinatorial transcriptional dynamics from gene expression data. Bioinform. Manfred Opper, Guido Sanguinetti
2010	J	`jnl`	Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster. BMC Syst. Biol. Michael Dewar, Visakan Kadirkamanathan, Manfred Opper, Guido Sanguinetti
2010	A	`conf`	Regret Bounds for Gaussian Process Bandit Problems. AISTATS Steffen Grünewälder, Jean-Yves Audibert, Manfred Opper, John Shawe-Taylor
2009	J	`jnl`	Optimal control as a graphical model inference problem CoRR Bert Kappen, Vicenç Gómez, Manfred Opper
2009	J	`jnl`	Perturbation Corrections in Approximate Inference: Mixture Modelling Applications. J. Mach. Learn. Res. Ulrich Paquet, Ole Winther, Manfred Opper
2009	J	`jnl`	Switching regulatory models of cellular stress response. Bioinform. Guido Sanguinetti, Andreas Ruttor, Manfred Opper, Cédric Archambeau
2009	J	`jnl`	The Variational Gaussian Approximation Revisited. Neural Comput. Manfred Opper, Cédric Archambeau
2008	—	`conf`	Improving on Expectation Propagation. NIPS Manfred Opper, Ulrich Paquet, Ole Winther
2007	—	`conf`	Gaussian Process Approximations of Stochastic Differential Equations. Gaussian Processes in Practice Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor
2007	—	`conf`	Variational Inference for Diffusion Processes. NIPS Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor
2007	—	`conf`	Variational inference for Markov jump processes. NIPS Manfred Opper, Guido Sanguinetti
2005	—	`conf`	An Approximate Inference Approach for the PCA Reconstruction Error. NIPS Manfred Opper
2005	J	`jnl`	Expectation Consistent Approximate Inference. J. Mach. Learn. Res. Manfred Opper, Ole Winther
2004	B	`conf`	Approximate Inference in Probabilistic Models. ALT Manfred Opper, Ole Winther
2004	—	`conf`	Expectation Consistent Free Energies for Approximate Inference. NIPS Manfred Opper, Ole Winther
2003	J	`jnl`	An Approximate Analytical Approach to Resampling Averages. J. Mach. Learn. Res. Dörthe Malzahn, Manfred Opper
2003	—	`conf`	Approximate Analytical Bootstrap Averages for Support Vector Classifiers. NIPS Dörthe Malzahn, Manfred Opper
2003	J	`jnl`	Learning curves and bootstrap estimates for inference with Gaussian processes: A statistical mechanics study. Complex. Dörthe Malzahn, Manfred Opper
2003	J	`jnl`	Tractable inference for probabilistic data models. Complex. Lehel Csató, Manfred Opper, Ole Winther
2003	—	`conf`	Variational Linear Response. NIPS Manfred Opper, Ole Winther
2002	—	`conf`	A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages. NIPS Dörthe Malzahn, Manfred Opper
2002	J	`jnl`	Drifting Games and Brownian Motion. J. Comput. Syst. Sci. Yoav Freund, Manfred Opper
2002	J	`jnl`	Region growing with pulse-coupled neural networks: an alternative to seeded region growing. IEEE Trans. Neural Networks Robert D. Stewart, Iris Fermin, Manfred Opper
2002	J	`jnl`	Sparse On-Line Gaussian Processes. Neural Comput. Lehel Csató, Manfred Opper
2001	—	`conf`	A Variational Approach to Learning Curves. NIPS Dörthe Malzahn, Manfred Opper
2001	—	`conf`	Asymptotic Universality for Learning Curves of Support Vector Machines. NIPS Manfred Opper, Robert Urbanczik
2001	C	`conf`	Learning Curves for Gaussian Processes Models: Fluctuations and Universality. ICANN Dörthe Malzahn, Manfred Opper
2001	C	`conf`	Online Approximations for Wind-Field Models. ICANN Lehel Csató, Dan Cornford, Manfred Opper
2001	—	`conf`	TAP Gibbs Free Energy, Belief Propagation and Sparsity. NIPS Lehel Csató, Manfred Opper, Ole Winther
2000	A*	`conf`	Continuous Drifting Games. COLT Yoav Freund, Manfred Opper
2000	J	`jnl`	Gaussian Processes for Classification: Mean-Field Algorithms. Neural Comput. Manfred Opper, Ole Winther
2000	—	`conf`	Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations. NIPS Dörthe Malzahn, Manfred Opper
2000	—	`conf`	Sparse Representation for Gaussian Process Models. NIPS Lehel Csató, Manfred Opper
1999	—	`conf`	Efficient Approaches to Gaussian Process Classification. NIPS Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther
1998	—	`conf`	Finite-Dimensional Approximation of Gaussian Processes. NIPS Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper
1998	—	`conf`	General Bounds on Bayes Errors for Regression with Gaussian Processes. NIPS Manfred Opper, Francesco Vivarelli
1998	—	`conf`	Mean Field Methods for Classification with Gaussian Processes. NIPS Manfred Opper, Ole Winther
1997	—	`conf`	Metric Entropy and Minimax Risk in Classification. Structures in Logic and Computer Science David Haussler, Manfred Opper
1996	—	`conf`	A Mean Field Algorithm for Bayes Learning in Large Feed-forward Neural Networks. NIPS Manfred Opper, Ole Winther
1996	—	`conf`	Dynamics of Training. NIPS Siegfried Bös, Manfred Opper
1995	A*	`conf`	General Bounds on the Mutual Information Between a Parameter and COLT David Haussler, Manfred Opper
1992	A*	`conf`	Query by Committee. COLT H. Sebastian Seung, Manfred Opper, Haim Sompolinsky
1991	A*	`conf`	Calculation of the Learning Curve of Bayes Optimal Classification Algorithm for Learning a Perceptron With Noise. COLT Manfred Opper, David Haussler
1991	—	`conf`	Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. NIPS David Haussler, Michael J. Kearns, Manfred Opper, Robert E. Schapire