bayesian-optimization/BayesianOptimization

Features

• Black-Box Optimization - Provides a framework for searching optimal input parameters in systems where the internal logic is unknown.
• Bayesian Optimization Libraries - Implements a Python library that uses Bayesian sampling algorithms to navigate complex parameter landscapes.
• Black-Box Maximizers - Iteratively tests parameters within defined bounds using statistical methods to maximize an unknown function.
• Gaussian Processes - Uses Gaussian processes to create a probabilistic surrogate model for predicting function values and uncertainty.
• Hyperparameter Tuning - Uses a statistical optimizer to tune machine learning parameters and minimize the number of required training runs.
• Hyperparameter Optimization - Automates the search for optimal configuration parameters to improve machine learning model performance.
• Bayesian Optimization Loops - Sequentially updates a surrogate model with new observations to refine the search for the global maximum.
• Black-Box Optimizers - Implements a wrapper that treats the target function as an opaque mapping to find optima without gradient information.
• Expensive Function Optimization - Finds peak values of computationally expensive operations by reducing the total number of required trials.
• Global Optimization Frameworks - Provides a system for maximizing target outputs by balancing exploration and exploitation within input bounds.
• Kernel Mappings - Uses kernel-based mappings to calculate similarity between input points for function value interpolation.
• Search Space Definition - Restricts the optimization process to a predefined set of parameter constraints to ensure efficient sampling.
• Automated Machine Learning - Automates the process of selecting optimal model parameters using statistical surrogate models.
• Acquisition Function Balancing - Balances the exploration of uncertain regions and exploitation of known high-value areas using an acquisition function.

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