Author

Wouter M. Kouw

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Preprint Aug 2026

Spike-based Belief Propagation in Nonlinear Dynamical Systems

This paper presents a Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control. Bayesian inference is widely regarded as a core computational principle of brain function, providing a normative framework for perception, decision-making, and learning under uncertai...

Sepideh Adamiat, Hongye Wang, Wouter M. Kouw et al. · 0 citations

Preprint Aug 2026

Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration

An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of greatest value, and paying for every meter of travel and the cost of every measurement it takes. Classica...

A. Meera, Pablo Lanillos, Wouter M. Kouw · 0 citations

#machine learning Open access Mar 2026

Curvature-Aware Expected Free Energy as an Acquisition Function for Bayesian Optimization

This work introduces a curvature-aware update law for Expected Free Energy and demonstrates it on a system identification problem in a Van der Pol oscillator and proves that Expected Free Energy has unbiased convergence guarantees for concave functions.

A. Meera, Wouter M. Kouw · IEEE Control Systems Letters · 1 citation

Preprint Aug 2026

A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression

Multi-output Gaussian process regression scales cubically in the number of observations times outputs, and dense kernel-matrix methods need bespoke handling whenever different outputs are observed at different inputs. We express multi-output Gaussian process regression as a Forney-style factor graph in which a nearest-...

Wouter W. L. Nuijten, Esther G. van Pelt, Albert Podusenko et al. · 0 citations