4 papers
Sharper Regret Bounds for Time-Varying Gaussian Process Bandits with Constant Exploration
Matthias Mandl, Hanne Kekkonen
We study Bayesian optimization in a time-varying environment where the unknown reward function evolves according to a Gaussian process drift model. Existing GP-UCB analyses in this…
Piecewise Deterministic Markov Processes for Bayesian Inference of PDE Coefficients
Leon Riccius, Iuri B. C. M. Rocha, Joris Bierkens +2
We develop a general framework for piecewise deterministic Markov process (PDMP) samplers that enables efficient Bayesian inference in non-linear inverse problems with expensive li…
Balancing Accuracy and Speed: A Multi-Fidelity Ensemble Kalman Filter with a Machine Learning Surrogate Model
Jeffrey van der Voort, Martin Verlaan, Hanne Kekkonen
Currently, more and more machine learning (ML) surrogates are being developed for computationally expensive physical models. In this work we investigate the use of a Multi-Fidelity…
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties
Leon Riccius, Iuri B. C. M. Rocha, Joris Bierkens +2
Recent advancements in Markov chain Monte Carlo (MCMC) sampling and surrogate modelling have significantly enhanced the feasibility of Bayesian analysis across engineering fields.…