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cs.LG2026
Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference
Mykola Lukashchuk, Kyrylo Yemets, Wouter M. Kouw +4
Stacking probabilistic building blocks into deeper architectures typically breaks closed-form inference. We show that closed-form inference can be preserved. We identify five facto…
cs.LG2026
Curvature-aware Expected Free Energy as an Acquisition Function for Bayesian Optimization
Ajith Anil Meera, Wouter Kouw
We propose an Expected Free Energy-based acquisition function for Bayesian optimization to solve the joint learning and optimization problem, i.e., optimize and learn the underlyin…
cs.LG2025
Bayesian autoregression to optimize temporal Matérn kernel Gaussian process hyperparameters
Wouter M. Kouw
Gaussian processes are important models in the field of probabilistic numerics. We present a procedure for optimizing Matérn kernel temporal Gaussian processes with respect to the…