137 citations · 184 across the 13 of their papers we have counts for
13 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…
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…
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…
Preference-Optimal Multi-Metric Weighting for Parallel Coordinate Plots
Chisa Mori, Shuhei Watanabe, Masaki Onishi +1
Parallel coordinate plots (PCPs) are a prevalent method to interpret the relationship between the control parameters and metrics. PCPs deliver such an interpretation by color grada…
Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks
Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman +1
While deep learning has celebrated many successes, its results often hinge on the meticulous selection of hyperparameters (HPs). However, the time-consuming nature of deep learning…
Python Wrapper for Simulating Multi-Fidelity Optimization on HPO Benchmarks without Any Wait
Shuhei Watanabe
Hyperparameter (HP) optimization of deep learning (DL) is essential for high performance. As DL often requires several hours to days for its training, HP optimization (HPO) of DL i…
PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces
Shuhei Watanabe, Archit Bansal, Frank Hutter
The recent rise in popularity of Hyperparameter Optimization (HPO) for deep learning has highlighted the role that good hyperparameter (HP) space design can play in training strong…
Python Tool for Visualizing Variability of Pareto Fronts over Multiple Runs
Shuhei Watanabe
Hyperparameter optimization is crucial to achieving high performance in deep learning. On top of the performance, other criteria such as inference time or memory requirement often…
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…
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator
Shuhei Watanabe, Noor Awad, Masaki Onishi +1
Hyperparameter optimization (HPO) is a vital step in improving performance in deep learning (DL). Practitioners are often faced with the trade-off between multiple criteria, such a…
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…
Warm Starting CMA-ES for Hyperparameter Optimization
Masahiro Nomura, Shuhei Watanabe, Youhei Akimoto +2
Hyperparameter optimization (HPO), formulated as black-box optimization (BBO), is recognized as essential for automation and high performance of machine learning approaches. The CM…