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cs.LG2026
Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights
Jixiang Qing, Henry Moss, Matthias Sachs
We consider the active learning problem where the goal is to learn an unknown function with low prediction error under an unknown Boltzmann distribution induced by the function its…
math.OC2026
Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
Becky Langdon, Gabriel D. Patrón, Chrysoula D. Kappatou +6
The optimisation of fed-batch (bio)chemical process recipes is subject to inherent, underlying, and unmeasurable fluctuations across batches, whose trajectories are difficult to mo…