Learning Skill-based Industrial Robot Tasks with User Priors. - Matthias Mayr; Carl Hvarfner; Konstantinos I. Chatzilygeroudis; Luigi Nardi; Volker Krüger (CASE, 2022)

$ pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. - Carl Hvarfner; Danny Stoll; Artur L. F. Souza; Marius Lindauer; Frank Hutter; Luigi Nardi (ICLR, 2022)

Joint Entropy Search For Maximally-Informed Bayesian Optimization. - Carl Hvarfner; Frank Hutter; Luigi Nardi (NeurIPS, 2022)

πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. - Carl Hvarfner; Danny Stoll; Artur L. F. Souza; Marius Lindauer; Frank Hutter; Luigi Nardi (CoRR, 2022)

Self-Correcting Bayesian Optimization through Bayesian Active Learning. - Carl Hvarfner; Erik Hellsten; Frank Hutter; Luigi Nardi (NeurIPS, 2023)

PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. - Neeratyoy Mallik; Edward Bergman; Carl Hvarfner; Danny Stoll; Maciej Janowski; Marius Lindauer; Luigi Nardi; Frank Hutter (NeurIPS, 2023)

High-dimensional Bayesian Optimization with Group Testing. - Erik Orm Hellsten; Carl Hvarfner; Leonard Papenmeier; Luigi Nardi (CoRR, 2023)

A General Framework for User-Guided Bayesian Optimization. - Carl Hvarfner; Frank Hutter; Luigi Nardi (ICLR, 2024)

Vanilla Bayesian Optimization Performs Great in High Dimensions. - Carl Hvarfner; Erik Orm Hellsten; Luigi Nardi (ICML, 2024)

CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization. - Jacob O. Tørring; Carl Hvarfner; Luigi Nardi; Magnus Själander (CoRR, 2024)

Bayesian Optimization Across the Spectrum of Knowledge: Enhancing Efficiency through Beliefs, Information and Assumptions. - Carl Hvarfner (2025)

Leveraging Axis-Aligned Subspaces for High-Dimensional Bayesian Optimization with Group Testing. - Erik Hellsten; Carl Hvarfner; Leonard Papenmeier; Luigi Nardi (CoRR, 2025)