Manfred Opper
Orcid: 0000-0003-2856-7589Affiliations:
- Technical University of Berlin, Department of Mathematics, Germany
According to our database1,
Manfred Opper
authored at least 98 papers
between 1991 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
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Online presence:
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on zbmath.org
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on orcid.org
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on id.loc.gov
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on dl.acm.org
On csauthors.net:
Bibliography
2024
Joint Message Detection, Channel, and User Position Estimation for Unsourced Random Access in Cell-Free Networks.
Proceedings of the 25th IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2024
A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification.
Proceedings of the IEEE International Symposium on Information Theory, 2024
Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
2023
Entropy, February, 2023
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
2022
GP-ETAS: semiparametric Bayesian inference for the spatio-temporal epidemic type aftershock sequence model.
Stat. Comput., 2022
Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects.
Entropy, 2022
CoRR, 2022
2021
Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation.
Entropy, 2021
CoRR, 2021
2020
PLoS Comput. Biol., 2020
Interacting Particle Solutions of Fokker-Planck Equations Through Gradient-Log-Density Estimation.
Entropy, 2020
CoRR, 2020
Understanding the dynamics of message passing algorithms: a free probability heuristics.
CoRR, 2020
CoRR, 2020
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020
2019
CoRR, 2019
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019
Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices.
Proceedings of the IEEE International Symposium on Information Theory, 2019
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019
2018
Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form Approximation.
Neural Comput., 2018
J. Mach. Learn. Res., 2018
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018
Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018
2017
An estimator for the relative entropy rate of path measures for stochastic differential equations.
J. Comput. Phys., 2017
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017
2016
Comput. Stat. Data Anal., 2016
2015
A Theory of Solving TAP Equations for Ising Models with General Invariant Random Matrices.
CoRR, 2015
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015
2014
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014
2013
Perturbative corrections for approximate inference in Gaussian latent variable models.
J. Mach. Learn. Res., 2013
Approximate Gaussian process inference for the drift function in stochastic differential equations.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013
Approximate inference in latent Gaussian-Markov models from continuous time observations.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013
Proceedings of the IEEE INFOCOM 2013, Turin, Italy, April 14-19, 2013, 2013
2012
Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012
Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions.
Comput. Stat., 2012
2011
Expectation Propagation with Factorizing Distributions: A Gaussian Approximation and Performance Results for Simple Models.
Neural Comput., 2011
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011
2010
A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems.
J. Signal Process. Syst., 2010
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010
A new variational radial basis function approximation for inference in multivariate diffusions.
Neurocomputing, 2010
Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster.
BMC Syst. Biol., 2010
Bioinform., 2010
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010
Approximate Inference for Stochastic Reaction processes.
Proceedings of the Learning and Inference in Computational Systems Biology., 2010
2009
J. Mach. Learn. Res., 2009
2008
Proceedings of the Advances in Neural Information Processing Systems 21, 2008
2007
Proceedings of the Gaussian Processes in Practice, 2007
Proceedings of the Advances in Neural Information Processing Systems 20, 2007
Proceedings of the Advances in Neural Information Processing Systems 20, 2007
2005
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005
2004
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004
Proceedings of the Algorithmic Learning Theory, 15th International Conference, 2004
2003
J. Mach. Learn. Res., 2003
Learning curves and bootstrap estimates for inference with Gaussian processes: A statistical mechanics study.
Complex., 2003
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003
2002
Region growing with pulse-coupled neural networks: an alternative to seeded region growing.
IEEE Trans. Neural Networks, 2002
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002
2001
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001
Proceedings of the Artificial Neural Networks, 2001
Proceedings of the Artificial Neural Networks, 2001
2000
Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000
Proceedings of the Advances in Neural Information Processing Systems 13, 2000
Continuous Drifting Games.
Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT 2000), June 28, 2000
1999
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999
1998
Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998
Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998
Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998
1997
Proceedings of the Structures in Logic and Computer Science, 1997
1996
Proceedings of the Advances in Neural Information Processing Systems 9, 1996
Proceedings of the Advances in Neural Information Processing Systems 9, 1996
1995
General Bounds on the Mutual Information Between a Parameter and <i>n</i> Conditionally Independent Observations.
Proceedings of the Eigth Annual Conference on Computational Learning Theory, 1995
1992
Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, 1992
1991
Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods.
Proceedings of the Advances in Neural Information Processing Systems 4, 1991
Calculation of the Learning Curve of Bayes Optimal Classification Algorithm for Learning a Perceptron With Noise.
Proceedings of the Fourth Annual Workshop on Computational Learning Theory, 1991