137 citations · 161 across the 5 of their papers we have counts for
7 papers
Optuna Constrained Tree-Structured Parzen Estimator Is a Joint Density Generalization of c-TPE
Shuhei Watanabe, Kaichi Irie
Constrained hyperparameter optimization (HPO) is common in practice, yet Optuna's widely used constrained TPE lacks algorithmic analysis. While c-TPE proposes an expected constrain…
Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces
Kaito Baba, Yoshihiko Ozaki, Shuhei Watanabe
We propose conditional PED-ANOVA (condPED-ANOVA), a principled framework for estimating hyperparameter importance (HPI) in conditional search spaces, where the presence or domain o…
c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization
Shuhei Watanabe, Frank Hutter
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 o…
Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance
Shuhei Watanabe
Recent scientific advances require complex experiment design, necessitating the meticulous tuning of many experiment parameters. Tree-structured Parzen estimator (TPE) is a widely…
OptunaHub: A Platform for Black-Box Optimization
Yoshihiko Ozaki, Shuhei Watanabe, Toshihiko Yanase
Black-box optimization (BBO) underpins advances in domains such as AutoML and Materials Informatics, yet implementations of algorithms and benchmarks remain fragmented across resea…
Batch Acquisition Function Evaluations and Decouple Optimizer Updates for Faster Bayesian Optimization
Kaichi Irie, Shuhei Watanabe, Masaki Onishi
Bayesian optimization (BO) efficiently finds high-performing parameters by maximizing an acquisition function, which models the promise of parameters. A major computational bottlen…