Author

Jeremias Knoblauch

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Preprint Oct 2026

Markov chain Monte Carlo for predictively oriented posteriors

The predictively oriented posterior offers principled uncertainty quantification, even under model misspecification. However, it does not admit an explicit density and therefore cannot be computed using classical Monte Carlo sampling algorithms. We remedy this by deriving an approximation to the predictively oriented p...

Yann McLatchie, Louis Sharrock, David T. Frazier et al. · 1 citation

#machine learning Preprint Oct 2026

Predictively Oriented Gaussian Process Posteriors

Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitioners to make a number of design decisions, such as the choice of the kernel and the observation model. Suboptimal choices can produce misspecified models that do not capture...

Callum Lau, Jeremias Knoblauch, Louis Sharrock · 0 citations

Preprint Aug 2026

Can Bayesian Optimization Efficiently Find a Strong Single Expert in Neural Thickets?

Results show that surrogate-guided search can substantially reduce the evaluation cost of gradient-free post-training while producing stronger deployable single experts.

Nigel Bastian Cendra, Abdelhamid Ezzerg, Fernando Julio Cendra et al. · 0 citations