11 papers
In-Context Learning for Latent Space Bayesian Optimization
Tuan A. Vu, Harri Lähdesmäki, Julien Martinelli
Bayesian optimization (BO) is a central tool for sample-efficient design, and latent-space Bayesian optimization (LSBO) extends it to structured objects such as molecules and prote…
Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization
Marshal Arijona Sinaga, Julien Martinelli, Samuel Kaski
Preferential Bayesian optimization (PBO) learns latent utilities from pairwise comparisons, but most existing methods assume homoscedastic comparison noise. This is inadequate in h…
Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes
Julien Martinelli, Maksim Sinelnikov, Harri Lähdesmäki +2
Dynamical modelling is central to many scientific domains, including pharmacometrics, systems biology, physiology, and epidemiology. In these settings, heterogeneity is often intri…
In-Context Multi-Objective Optimization
Xinyu Zhang, Conor Hassan, Julien Martinelli +2
Balancing competing objectives is omnipresent across disciplines, from drug design to autonomous systems. Multi-objective Bayesian optimization is a promising solution for such exp…
Online Sharp-Calibrated Bayesian Optimization
Marshal Arijona Sinaga, Julien Martinelli, Teemu Turpeinen +1
Bayesian optimization (BO) is a widely used framework for optimizing expensive black-box functions, commonly based on Gaussian process (GP) surrogate models. Its effectiveness reli…
In-Context Black-Box Optimization with Unreliable Feedback
Nicolas Samuel Blumer, Julien Martinelli, Samuel Kaski
Black-box optimization in science and engineering often comes with side information: experts, simulators, pretrained predictors, or heuristics can suggest which candidates look pro…