
Interacting particle solutions of FokkerPlanck equations through gradientlogdensity estimation
FokkerPlanck equations are extensively employed in various scientific f...
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GPETAS: Semiparametric Bayesian inference for the spatiotemporal Epidemic Type Aftershock Sequence model
The spatiotemporal Epidemic Type Aftershock Sequence (ETAS) model is wi...
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A Dynamical MeanField Theory for Learning in Restricted Boltzmann Machines
We define a messagepassing algorithm for computing magnetizations in Re...
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Automated Augmented Conjugate Inference for Nonconjugate Gaussian Process Models
We propose automated augmented conjugate inference, a new inference meth...
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Understanding the dynamics of message passing algorithms: a free probability heuristics
We use freeness assumptions of random matrix theory to analyze the dynam...
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Analysis of Bayesian Inference Algorithms by the Dynamical Functional Approach
We analyze the dynamics of an algorithm for approximate inference with l...
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Tightening Bounds for Variational Inference by Revisiting Perturbation Theory
Variational inference has become one of the most widely used methods in ...
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MultiClass Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
We propose a new scalable multiclass Gaussian process classification ap...
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Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices
We propose an iterative algorithm for solving the ThoulessAndersonPalm...
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Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
We present an approximate Bayesian inference approach for estimating the...
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Efficient Bayesian Inference for a Gaussian Process Density Model
We reconsider a nonparametric density model based on Gaussian processes....
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Efficient Gaussian Process Classification Using PolyaGamma Data Augmentation
We propose an efficient stochastic variational approach to GP classifica...
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Expectation Propagation for Approximate Inference: Free Probability Framework
We study asymptotic properties of expectation propagation (EP)  a metho...
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Perturbative Black Box Variational Inference
Black box variational inference (BBVI) with reparameterization gradients...
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Inverse Ising problem in continuous time: A latent variable approach
We consider the inverse Ising problem, i.e. the inference of network cou...
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A statistical physics approach to learning curves for the Inverse Ising problem
Using methods of statistical physics, we analyse the error of learning c...
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Approximate Bayes learning of stochastic differential equations
We introduce a nonparametric approach for estimating drift and diffusion...
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Optimal Encoding and Decoding for Point Process Observations: an Approximate ClosedForm Filter
The process of dynamic state estimation (filtering) based on point proce...
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Variational perturbation and extended Plefka approaches to dynamics on random networks: the case of the kinetic Ising model
We describe and analyze some novel approaches for studying the dynamics ...
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Expectation propagation for continuous time stochastic processes
We consider the inverse problem of reconstructing the posterior measure ...
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Expectation Propagation
Variational inference is a powerful concept that underlies many iterativ...
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Optimal Population Codes for Control and Estimation
Agents acting in the natural world aim at selecting appropriate actions ...
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Temporal Autoencoding Improves Generative Models of Time Series
Restricted Boltzmann Machines (RBMs) are generative models which can lea...
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Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models
Expectation Propagation (EP) provides a framework for approximate infere...
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Manfred Opper
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Professor Department of Computer Science at Technische Universität Berlin