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ORCIDManfred Opper
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- affiliation: Technical University of Berlin, Department of Mathematics, Germany
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2020 – today
- 2024
[c55]Rembert Daems, Manfred Opper, Guillaume Crevecoeur, Tolga Birdal:
Variational Inference for SDEs Driven by Fractional Noise. ICLR 2024- [c54]Ludwig Winkler, Lorenz Richter, Manfred Opper:
Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models. ICML 2024 - [c53]Burak Çakmak, Yue M. Lu, Manfred Opper:
A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification. ISIT 2024: 747-752 - [c52]Eleni Gkiouzepi, Burak Çakmak, Manfred Opper, Giuseppe Caire:
Joint Message Detection, Channel, and User Position Estimation for Unsourced Random Access in Cell-Free Networks. SPAWC 2024: 151-155 - [i27]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 abs/2402.08676 (2024) - [i26]Ludwig Winkler, Lorenz Richter, Manfred Opper:
Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models. CoRR abs/2405.03549 (2024) - [i25]Eleni Gkiouzepi, Burak Çakmak, Manfred Opper, Giuseppe Caire:
Joint Message Detection, Channel, and User Position Estimation for Unsourced Random Access in Cell-Free Networks. CoRR abs/2408.08045 (2024) - 2023
- [j30]Ludwig Winkler, César Ojeda, Manfred Opper:
A Score-Based Approach for Training Schrödinger Bridges for Data Modelling. Entropy 25(2): 316 (2023) - [i24]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 abs/2304.12290 (2023) - [i23]Rembert Daems, Manfred Opper, Guillaume Crevecoeur, Tolga Birdal:
Variational Inference for SDEs Driven by Fractional Noise. CoRR abs/2310.12975 (2023) - 2022
- [j29]Noa Malem-Shinitski, César Ojeda, Manfred Opper:
Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects. Entropy 24(3): 356 (2022) - [j28]Ludwig Winkler, César Ojeda, Manfred Opper:
Stochastic Control for Bayesian Neural Network Training. Entropy 24(8): 1097 (2022) - [j27]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): 29 (2022) - [i22]Burak Çakmak, Yue M. Lu, Manfred Opper:
Analysis of Random Sequential Message Passing Algorithms for Approximate Inference. CoRR abs/2202.08198 (2022) - 2021
- [j26]Théo Galy-Fajou, Valerio Perrone, Manfred Opper:
Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation. Entropy 23(8): 990 (2021) - [i21]Burak Çakmak, Manfred Opper:
Exact solution to the random sequential dynamics of a message passing algorithm. CoRR abs/2101.01571 (2021) - [i20]Noa Malem-Shinitski, César Ojeda, Manfred Opper:
Nonlinear Hawkes Process with Gaussian Process Self Effects. CoRR abs/2105.09618 (2021) - [i19]Théo Galy-Fajou, Manfred Opper:
Adaptive Inducing Points Selection For Gaussian Processes. CoRR abs/2107.10066 (2021) - 2020
- [j25]Dimitra Maoutsa, Sebastian Reich, Manfred Opper:
Interacting Particle Solutions of Fokker-Planck Equations Through Gradient-Log-Density Estimation. Entropy 22(8): 802 (2020) - [j24]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) - [c51]Théo Galy-Fajou, Florian Wenzel, Manfred Opper:
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. AISTATS 2020: 3025-3035 - [i18]Burak Çakmak, Manfred Opper:
Analysis of Bayesian Inference Algorithms by the Dynamical Functional Approach. CoRR abs/2001.04918 (2020) - [i17]Manfred Opper, Burak Çakmak:
Understanding the dynamics of message passing algorithms: a free probability heuristics. CoRR abs/2002.02533 (2020) - [i16]Théo Galy-Fajou, Florian Wenzel, Manfred Opper:
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. CoRR abs/2002.11451 (2020) - [i15]Burak Çakmak, Manfred Opper:
A Dynamical Mean-Field Theory for Learning in Restricted Boltzmann Machines. CoRR abs/2005.01560 (2020)
2010 – 2019
- 2019
- [c50]Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper:
Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation. AAAI 2019: 5417-5424 - [c49]Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann:
Statistical physics of learning and inference. ESANN 2019 - [c48]Burak Çakmak, Manfred Opper:
Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices. ISIT 2019: 1297-1301 - [c47]Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper:
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation. UAI 2019: 755-765 - [i14]Burak Çakmak, Manfred Opper:
Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices. CoRR abs/1901.08583 (2019) - [i13]Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper:
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation. CoRR abs/1905.09670 (2019) - [i12]Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt:
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory. CoRR abs/1910.00069 (2019) - 2018
- [j23]Christian Donner, Manfred Opper:
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes. J. Mach. Learn. Res. 19: 67:1-67:34 (2018) - [j22]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) - [c46]Burak Çakmak, Manfred Opper:
Expectation Propagation for Approximate Inference: Free Probability Framework. ISIT 2018: 1276-1280 - [c45]Christian Donner, Manfred Opper:
Efficient Bayesian Inference for a Gaussian Process Density Model. UAI 2018: 53-62 - [i11]Burak Çakmak, Manfred Opper:
Expectation Propagation for Approximate Inference: Free Probability Framework. CoRR abs/1801.05411 (2018) - [i10]Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper:
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation. CoRR abs/1802.06383 (2018) - [i9]Christian Donner, Manfred Opper:
Efficient Bayesian Inference for a Gaussian Process Density Model. CoRR abs/1805.11494 (2018) - [i8]Christian Donner, Manfred Opper:
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes. CoRR abs/1808.00831 (2018) - 2017
- [j21]Manfred Opper:
An estimator for the relative entropy rate of path measures for stochastic differential equations. J. Comput. Phys. 330: 127-133 (2017) - [c44]Burak Çakmak, Manfred Opper, Ole Winther, Bernard H. Fleury:
Dynamical functional theory for compressed sensing. ISIT 2017: 2143-2147 - [c43]Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt:
Perturbative Black Box Variational Inference. NIPS 2017: 5079-5088 - [i7]Burak Çakmak, Manfred Opper, Ole Winther, Bernard H. Fleury:
Dynamical Functional Theory for Compressed Sensing. CoRR abs/1705.04284 (2017) - [i6]Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt:
Perturbative Black Box Variational Inference. CoRR abs/1709.07433 (2017) - 2016
- [j20]Gina Gruenhage, Manfred Opper, Simon Barthelmé:
Visualizing the effects of a changing distance on data using continuous embeddings. Comput. Stat. Data Anal. 104: 51-65 (2016) - [i5]Burak Çakmak, Manfred Opper, Bernard H. Fleury, Ole Winther:
Self-Averaging Expectation Propagation. CoRR abs/1608.06602 (2016) - 2015
- [c42]Yuval Harel, Ron Meir, Manfred Opper:
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding. NIPS 2015: 1603-1611 - [i4]Manfred Opper, Burak Çakmak, Ole Winther:
A Theory of Solving TAP Equations for Ising Models with General Invariant Random Matrices. CoRR abs/1509.01229 (2015) - 2014
- [c41]Florian Stimberg, Andreas Ruttor, Manfred Opper:
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data. NIPS 2014: 730-738 - [c40]Alex K. Susemihl, Ron Meir, Manfred Opper:
Optimal Neural Codes for Control and Estimation. NIPS 2014: 2987-2995 - [i3]Alex K. Susemihl, Ron Meir, Manfred Opper:
Optimal Population Codes for Control and Estimation. CoRR abs/1406.7179 (2014) - 2013
- [j19]Manfred Opper, Ulrich Paquet, Ole Winther:
Perturbative corrections for approximate inference in Gaussian latent variable models. J. Mach. Learn. Res. 14(1): 2857-2898 (2013) - [c39]Hilbert J. Kappen, Vicenç Gómez, Manfred Opper:
Optimal Control as a Graphical Model Inference Problem. ICAPS 2013 - [c38]Sven Wiethölter, Andreas Ruttor, Uwe Bergemann, Manfred Opper, Adam Wolisz:
DARA: Estimating the behavior of data rate adaptation algorithms in WLAN hotspots. INFOCOM 2013: 280-284 - [c37]Botond Cseke, Manfred Opper, Guido Sanguinetti:
Approximate inference in latent Gaussian-Markov models from continuous time observations. NIPS 2013: 971-979 - [c36]Andreas Ruttor, Philipp Batz, Manfred Opper:
Approximate Gaussian process inference for the drift function in stochastic differential equations. NIPS 2013: 2040-2048 - [i2]Chris Häusler, Alex K. Susemihl, Martin P. Nawrot, Manfred Opper:
Temporal Autoencoding Improves Generative Models of Time Series. CoRR abs/1309.3103 (2013) - 2012
- [j18]Yuan Shen, Dan Cornford, Manfred Opper, Cédric Archambeau:
Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions. Comput. Stat. 27(1): 149-176 (2012) - [j17]Hilbert J. Kappen, Vicenç Gómez, Manfred Opper:
Optimal control as a graphical model inference problem. Mach. Learn. 87(2): 159-182 (2012) - [c35]Florian Stimberg, Andreas Ruttor, Manfred Opper:
Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach. AISTATS 2012: 1117-1124 - 2011
- [j16]Fabiano L. Ribeiro, Manfred Opper:
Expectation Propagation with Factorizing Distributions: A Gaussian Approximation and Performance Results for Simple Models. Neural Comput. 23(4): 1047-1069 (2011) - [c34]Alex K. Susemihl, Ron Meir, Manfred Opper:
Analytical Results for the Error in Filtering of Gaussian Processes. NIPS 2011: 2303-2311 - [c33]Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor:
Inference in continuous-time change-point models. NIPS 2011: 2717-2725 - 2010
- [j15]Manfred Opper, Guido Sanguinetti:
Learning combinatorial transcriptional dynamics from gene expression data. Bioinform. 26(13): 1623-1629 (2010) - [j14]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: 21 (2010) - [j13]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): 1186-1198 (2010) - [j12]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): 51-59 (2010) - [c32]Manfred Opper, Andreas Ruttor, Guido Sanguinetti:
Approximate inference in continuous time Gaussian-Jump processes. NIPS 2010: 1831-1839 - [c31]Steffen Grünewälder, Jean-Yves Audibert, Manfred Opper, John Shawe-Taylor:
Regret Bounds for Gaussian Process Bandit Problems. AISTATS 2010: 273-280 - [c30]Andreas Ruttor, Manfred Opper:
Approximate parameter inference in a stochastic reaction-diffusion model. AISTATS 2010: 669-676 - [p1]Andreas Ruttor, Guido Sanguinetti, Manfred Opper:
Approximate Inference for Stochastic Reaction processes. Learning and Inference in Computational Systems Biology 2010: 277-296
2000 – 2009
- 2009
- [j11]Guido Sanguinetti, Andreas Ruttor, Manfred Opper, Cédric Archambeau:
Switching regulatory models of cellular stress response. Bioinform. 25(10): 1280-1286 (2009) - [j10]Ulrich Paquet, Ole Winther, Manfred Opper:
Perturbation Corrections in Approximate Inference: Mixture Modelling Applications. J. Mach. Learn. Res. 10: 1263-1304 (2009) - [j9]Manfred Opper, Cédric Archambeau:
The Variational Gaussian Approximation Revisited. Neural Comput. 21(3): 786-792 (2009) - [i1]Bert Kappen, Vicenç Gómez, Manfred Opper:
Optimal control as a graphical model inference problem. CoRR abs/0901.0633 (2009) - 2008
- [c29]Manfred Opper, Ulrich Paquet, Ole Winther:
Improving on Expectation Propagation. NIPS 2008: 1241-1248 - 2007
- [c28]Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor:
Variational Inference for Diffusion Processes. NIPS 2007: 17-24 - [c27]Manfred Opper, Guido Sanguinetti:
Variational inference for Markov jump processes. NIPS 2007: 1105-1112 - [c26]Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor:
Gaussian Process Approximations of Stochastic Differential Equations. Gaussian Processes in Practice 2007: 1-16 - 2005
- [j8]Manfred Opper, Ole Winther:
Expectation Consistent Approximate Inference. J. Mach. Learn. Res. 6: 2177-2204 (2005) - [c25]Manfred Opper:
An Approximate Inference Approach for the PCA Reconstruction Error. NIPS 2005: 1035-1042 - 2004
- [c24]Manfred Opper, Ole Winther:
Approximate Inference in Probabilistic Models. ALT 2004: 494-504 - [c23]Manfred Opper, Ole Winther:
Expectation Consistent Free Energies for Approximate Inference. NIPS 2004: 1001-1008 - 2003
- [j7]Dörthe Malzahn, Manfred Opper:
Learning curves and bootstrap estimates for inference with Gaussian processes: A statistical mechanics study. Complex. 8(4): 57-63 (2003) - [j6]Lehel Csató, Manfred Opper, Ole Winther:
Tractable inference for probabilistic data models. Complex. 8(4): 64-68 (2003) - [j5]Dörthe Malzahn, Manfred Opper:
An Approximate Analytical Approach to Resampling Averages. J. Mach. Learn. Res. 4: 1151-1173 (2003) - [c22]Manfred Opper, Ole Winther:
Variational Linear Response. NIPS 2003: 1157-1164 - [c21]Dörthe Malzahn, Manfred Opper:
Approximate Analytical Bootstrap Averages for Support Vector Classifiers. NIPS 2003: 1189-1196 - 2002
- [j4]Yoav Freund, Manfred Opper:
Drifting Games and Brownian Motion. J. Comput. Syst. Sci. 64(1): 113-132 (2002) - [j3]Lehel Csató, Manfred Opper:
Sparse On-Line Gaussian Processes. Neural Comput. 14(3): 641-668 (2002) - [j2]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): 1557-1562 (2002) - [c20]Dörthe Malzahn, Manfred Opper:
A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages. NIPS 2002: 327-334 - 2001
- [c19]Dörthe Malzahn, Manfred Opper:
Learning Curves for Gaussian Processes Models: Fluctuations and Universality. ICANN 2001: 271-276 - [c18]Lehel Csató, Dan Cornford, Manfred Opper:
Online Approximations for Wind-Field Models. ICANN 2001: 300-307 - [c17]Dörthe Malzahn, Manfred Opper:
A Variational Approach to Learning Curves. NIPS 2001: 463-469 - [c16]Manfred Opper, Robert Urbanczik:
Asymptotic Universality for Learning Curves of Support Vector Machines. NIPS 2001: 479-486 - [c15]Lehel Csató, Manfred Opper, Ole Winther:
TAP Gibbs Free Energy, Belief Propagation and Sparsity. NIPS 2001: 657-663 - 2000
- [j1]Manfred Opper, Ole Winther:
Gaussian Processes for Classification: Mean-Field Algorithms. Neural Comput. 12(11): 2655-2684 (2000) - [c14]Yoav Freund, Manfred Opper:
Continuous Drifting Games. COLT 2000: 126-132 - [c13]Dörthe Malzahn, Manfred Opper:
Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations. NIPS 2000: 273-279 - [c12]Lehel Csató, Manfred Opper:
Sparse Representation for Gaussian Process Models. NIPS 2000: 444-450
1990 – 1999
- 1999
- [c11]Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther:
Efficient Approaches to Gaussian Process Classification. NIPS 1999: 251-257 - 1998
- [c10]Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper:
Finite-Dimensional Approximation of Gaussian Processes. NIPS 1998: 218-224 - [c9]Manfred Opper, Francesco Vivarelli:
General Bounds on Bayes Errors for Regression with Gaussian Processes. NIPS 1998: 302-308 - [c8]Manfred Opper, Ole Winther:
Mean Field Methods for Classification with Gaussian Processes. NIPS 1998: 309-315 - 1997
- [c7]David Haussler, Manfred Opper:
Metric Entropy and Minimax Risk in Classification. Structures in Logic and Computer Science 1997: 212-235 - 1996
- [c6]Siegfried Bös, Manfred Opper:
Dynamics of Training. NIPS 1996: 141-147 - [c5]Manfred Opper, Ole Winther:
A Mean Field Algorithm for Bayes Learning in Large Feed-forward Neural Networks. NIPS 1996: 225-231 - 1995
- [c4]David Haussler, Manfred Opper:
General Bounds on the Mutual Information Between a Parameter and n Conditionally Independent Observations. COLT 1995: 402-411 - 1992
- [c3]H. Sebastian Seung, Manfred Opper, Haim Sompolinsky:
Query by Committee. COLT 1992: 287-294 - 1991
- [c2]Manfred Opper, David Haussler:
Calculation of the Learning Curve of Bayes Optimal Classification Algorithm for Learning a Perceptron With Noise. COLT 1991: 75-87 - [c1]David Haussler, Michael J. Kearns, Manfred Opper, Robert E. Schapire:
Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. NIPS 1991: 855-862
Coauthor Index
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