The field of Bayesian optimization and uncertainty quantification is witnessing significant developments, with a focus on improving the efficiency and accuracy of optimization algorithms. Researchers are exploring new probabilistic models, such as spectral mixture kernels and probabilistic circuits, to enhance the performance of Bayesian optimization methods. Additionally, there is a growing interest in interactive hyperparameter optimization, where human feedback is incorporated to improve the optimization process. The development of new uncertainty bounds and confidence intervals is also a key area of research, with applications in safety-critical contexts. Noteworthy papers include: Spectral Mixture Kernels for Bayesian Optimization, which introduces a novel Gaussian Process-based BO method that achieves significant improvements in efficiency and optimization performance. Another notable paper is STaR-Bets: Sequential Target-Recalculating Bets for Tighter Confidence Intervals, which proposes a betting-based algorithm to compute confidence intervals that empirically outperforms competitors.