arXiv:2606. 09889v1 Announce Type: new Abstract: Constrained hyperparameter optimization (HPO) is common in practice, yet Optuna's widely used constrained TPE lacks algorithmic analysis.
By Shuhei Watanabe, Kaichi Irie
arXiv:2606. 07841v1 Announce Type: cross Abstract: Black-box variational inference (BBVI) is a methodology for posterior approximation that relies on stochastic optimization.
By Trevor Campbell, Jonathan H. Huggins, Kyurae Kim, Charles C. Margossian
arXiv:2511. 02570v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO).
By Lukas Fehring, Marcel Wever, Maximilian Splieth\"over, Leona Hennig, Henning Wachsmuth, Marius Lindauer
arXiv:2606. 04866v1 Announce Type: new Abstract: Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing concerns about scalability and energy efficiency.
By Leona Hennig, Jasmin Brandt, Lukas Fehring, Barbara Hammer, Marius Lindauer, Marcel Wever
The paper introduces Bayesian Optimization (BO) techniques that incorporate rich auxiliary information—such as training curves, expert notes, images, and prior knowledge—using large language models (LLMs). Three new methods are proposed to integrate this auxiliary data into BO, and they are evaluated on hyperparameter optimization benchmarks and a real-world nuclear fusion task. The results show that these LLM-enhanced BO methods consistently outperform standard BO and existing LLM-based optimization approaches.
By Tejus Gupta, Efe Mert Karag\"ozl\"u, Rohit Sonker, Barnab\'as P\'oczos, Jeff Schnieder
The article investigates how Bayesian optimization can improve the ACTS parameter optimization suite for charged‑particle reconstruction. By comparing Expected Improvement and Upper Confidence Bound with TPE and random search on an eight‑parameter problem, extending the best method to fifteen parameters, and applying Expected Hypervolume Improvement for multi‑objective tuning, the study shows that Bayesian acquisition methods find strong configurations earlier and maintain advantages in held‑out validation. The results demonstrate that Bayesian optimization enhances ACTS auto‑tuning through more efficient evaluations, broader search spaces, and the ability to select from non‑dominated trade‑off solutions.
By Chance LaVoie, Qi Bin Lei, Rocky Bala Garg, Lauren Tompkins