Bayesian optimization (BO) with Gaussian processes (GPs) surrogate models is widely used
to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we …

We demonstrate that a wide array of machine learning algorithms are specific instances of
one single paradigm: reciprocal learning. These instances range from active learning over …

Bayesian optimisation for real-world problems is often performed interactively with human
experts, and integrating their domain knowledge is key to accelerate the optimisation …

We provide a theoretical and computational investigation of the Gamma-Maximin method
with soft revision, which was recently proposed as a robust criterion for pseudo-label …

Hyperparameter optimization plays a pivotal role in enhancing the predictive performance
and generalization capabilities of ML models. However, in many applications, we do not …

Optimizing costly black-box functions within a constrained evaluation budget presents
significant challenges in many real-world applications. Surrogate Optimization (SO) is a …