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04/24/2023
Inference in Linear Observations with Multiple Signal Sources: Analysis of Approximate Message Passing and Applications to Unsourced Random Access in Cell-Free Systems
Here we consider a problem of multiple measurement vector (MMV) compress...
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02/16/2022
Analysis of Random Sequential Message Passing Algorithms for Approximate Inference
We analyze the dynamics of a random sequential message passing algorithm...
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12/10/2021
Deterministic particle flows for constraining stochastic nonlinear systems
Devising optimal interventions for constraining stochastic systems is a ...
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10/25/2021
Deterministic particle flows for constraining SDEs
Devising optimal interventions for diffusive systems often requires the ...
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07/21/2021
Adaptive Inducing Points Selection For Gaussian Processes
Gaussian Processes (GPs) are flexible non-parametric models with strong ...
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05/20/2021
Nonlinear Hawkes Process with Gaussian Process Self Effects
Traditionally, Hawkes processes are used to model time–continuous point ...
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01/05/2021
Exact solution to the random sequential dynamics of a message passing algorithm
We analyze the random sequential dynamics of a message passing algorithm...
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06/01/2020
Interacting particle solutions of Fokker-Planck equations through gradient-log-density estimation
Fokker-Planck equations are extensively employed in various scientific f...
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05/26/2020
GP-ETAS: Semiparametric Bayesian inference for the spatio-temporal Epidemic Type Aftershock Sequence model
The spatio-temporal Epidemic Type Aftershock Sequence (ETAS) model is wi...
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05/04/2020
A Dynamical Mean-Field Theory for Learning in Restricted Boltzmann Machines
We define a message-passing algorithm for computing magnetizations in Re...
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02/26/2020
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
We propose automated augmented conjugate inference, a new inference meth...
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02/03/2020
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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01/14/2020
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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09/30/2019
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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05/23/2019
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
We propose a new scalable multi-class Gaussian process classification ap...
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01/24/2019
Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices
We propose an iterative algorithm for solving the Thouless-Anderson-Palm...
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08/02/2018
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
We present an approximate Bayesian inference approach for estimating the...
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05/29/2018
Efficient Bayesian Inference for a Gaussian Process Density Model
We reconsider a nonparametric density model based on Gaussian processes....
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02/18/2018
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
We propose an efficient stochastic variational approach to GP classifica...
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01/16/2018
Expectation Propagation for Approximate Inference: Free Probability Framework
We study asymptotic properties of expectation propagation (EP) - a metho...
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09/21/2017
Perturbative Black Box Variational Inference
Black box variational inference (BBVI) with reparameterization gradients...
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09/04/2017
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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05/15/2017
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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02/17/2017
Approximate Bayes learning of stochastic differential equations
We introduce a nonparametric approach for estimating drift and diffusion...
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09/12/2016
Optimal Encoding and Decoding for Point Process Observations: an Approximate Closed-Form Filter
The process of dynamic state estimation (filtering) based on point proce...
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07/28/2016
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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12/18/2015
Expectation propagation for continuous time stochastic processes
We consider the inverse problem of reconstructing the posterior measure ...
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09/22/2014
Expectation Propagation
Variational inference is a powerful concept that underlies many iterativ...
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06/27/2014
Optimal Population Codes for Control and Estimation
Agents acting in the natural world aim at selecting appropriate actions ...
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09/12/2013
Temporal Autoencoding Improves Generative Models of Time Series
Restricted Boltzmann Machines (RBMs) are generative models which can lea...
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01/12/2013
