arXiv AI June 2, 2026 By Shuhei Watanabe, Frank Hutter

c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization

arXiv:2211. 14411v5 Announce Type: replace-cross Abstract: Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms and real-world applications often impose some constraints, such as on memory usage or latency, on top of the performance requirement.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 10

Optuna Constrained Tree-Structured Parzen Estimator Is a Joint Density Generalization of c-TPE

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 Machine Learning

Jun 9

Large-scale empirical tuning and comparison of default optimizers for variational inference

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

benchmarks

arXiv Machine Learning

Aug 3

Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization

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

benchmarks

arXiv Machine Learning

Jun 4

Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization

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

benchmarks

arXiv Machine Learning

Jul 31

Towards Stability of Parameter-Free Optimization

arXiv:2405. 04376v4 Announce Type: replace Abstract: Hyperparameter tuning, particularly the selection of an appropriate learning rate in adaptive gradient training methods, remains a challenge.

By Yijiang Pang, Shuyang Yu, Bao Hoang, Jiayu Zhou

arXiv AI

Jul 28

Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds

arXiv:2607. 23448v1 Announce Type: cross Abstract: Expensive constrained optimization problems in real-world industry design often involve constraint thresholds that are difficult to determine in advance.

By Jin Wang, Xi Lin, Handing Wang

benchmarks

Related stories

Optuna Constrained Tree-Structured Parzen Estimator Is a Joint Density Generalization of c-TPE

Large-scale empirical tuning and comparison of default optimizers for variational inference

Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization

Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization

Towards Stability of Parameter-Free Optimization

Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds