Hyperparameter Optimization
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Benchmarks
Bayesmark
Most implemented
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
Optuna: A Next-generation Hyperparameter Optimization Framework
Optimizing Millions of Hyperparameters by Implicit Differentiation
Are GANs Created Equal? A Large-Scale Study
A Tutorial on Bayesian Optimization
Papers
Calibrating Prediction Timeliness Through Multi-Objective Hyperparameter Optimization for Remaining Useful Life Prediction
In predictive maintenance, early and late RUL prediction errors carry asymmetric consequences, yet hyperparameter optimization typically targets a single accuracy metric that treats both directions equally. This study tr…
Hyperparameter OptimizationAn Explainable Machine Learning Framework for Predicting Blood-Brain Barrier Permeability Using Molecular Descriptors
Blood-brain barrier (BBB) permeability is a critical determinant in the development of central nervous system therapeutics because it directly influences the ability of drug candidates to reach their target sites within …
Hyperparameter OptimizationFeature ImportanceInvestigating Hyperparameter Optimization and Transferability for ES-HyperNEAT: A TPE Approach
Neuroevolution of Augmenting Topologies (NEAT) and its advanced version, Evolvable-Substrate HyperNEAT (ES-HyperNEAT), have shown great potential in developing neural networks. However, their effectiveness heavily depend…
Hyperparameter OptimizationDistributional Extrapolation for Interactions
Predicting combinatorial effects from limited-range observations is a fundamental challenge in many scientific domains, including drug discovery and hyperparameter optimization. We study combinatorial extrapolation, wher…
Hyperparameter OptimizationDrug DiscoveryTRACE-CASH: Trial-History-Conditioned Reinforcement Learning for Adaptive Configuration Exploration in Time-Series CASH
Combined algorithm selection and hyperparameter optimization (CASH) searches a conditional space in which the selected model determines which hyperparameters are active. In time-series forecasting, temporal choices, chro…
Hyperparameter OptimizationReinforcement LearningOn the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization
Machine learning (ML) has become an indispensable part of modern engineering design workflows. A crucial step in training an ML model is the selection of the loss function which can be systematically formulated via vario…
Hyperparameter OptimizationGaussian Processes