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BOHB (Bayesian Optimization HyperBand)

Hybrid algorithm combining Bayesian optimization for probabilistic modeling and HyperBand for adaptive resource allocation, enabling efficient hyperparameter search.

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Surrogate Model

Probabilistic model (typically GP or TPE) that approximates the expensive-to-evaluate objective function to guide hyperparameter search.

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Acquisition Function

Function that balances exploration and exploitation to determine the next hyperparameter configurations to evaluate.

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TPE (Tree-structured Parzen Estimator)

Non-parametric surrogate model using kernel density estimators to model hyperparameter distributions.

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Computational Budget

Limited resource (time, episodes, data) allocated to the evaluation of hyperparameter configurations in BOHB.

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Bandit-Based Allocation

Resource allocation strategy inspired by multi-armed bandits to optimize the exploration-exploitation trade-off.

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Hyperparameter Importance

Measure of each hyperparameter's impact on model performance, used to prioritize search in BOHB.

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Parallel Evaluation

Ability to evaluate multiple hyperparameter configurations simultaneously to accelerate optimization in BOHB.

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Thompson Sampling

Method of selection that samples configurations according to their probability of being optimal according to the surrogate model.

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Bracket Configuration

Specific parameter setting of HyperBand defining the number of configurations and the evolution of the budget for a given bracket.

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