> ML_ALGORITHM // GAUSSIAN-PROCESS-BAYESIAN-OPTIMIZATION-GP-BO_v1.0
Gaussian Process Bayesian Optimization (GP-BO)
Sample-efficient black-box optimization framework using Gaussian processes to model expensive evaluation landscapes and guide sampling via acquisition functions.
Bayesian Optimizationevolutionary-searchhigh-intrinsicsmall (<1k)
Back to All AlgorithmsComputational Complexity
Training Complexity:O(trials^3) exact GP inference
Inference Complexity:O(trials * acquisition_points)
Hardware Profile
CPU Friendly:Yes
Requires GPU:No
Memory Footprint:low
Interpretability & Data
Interpretability Tier:high-intrinsic
Training Data Needs:small (<1k)
Interpretability Assessment
GP posterior provides mean prediction and calibrated epistemic uncertainty bounds at all unobserved points.
Suitable Tasks & Supported Modalities
Suitable Tasks:
hyperparameter tuningexpensive blackbox experimentshardware tuning
Supported Modalities:
tabular
Implementing Libraries
Foundational Literature
Common Pitfalls & Warnings
- Inverting the N x N covariance matrix scales cubically O(N^3), making standard GP-BO stall when trials exceed 500-1000