Active Learning focuses on improving the overall accuracy of the function model by selecting areas with high uncertainty estimated through Gaussian Process
Active Learning aims to model the entire function, while Bayesian Optimization aims to find the optimal point of a black-box function considering Acquisition Function while both use Gaussian Process as a surrogate model (with a prior over the space of objective functions) which serves as a surrogate for function approximation, uncertainty estimation, and uncertainty reduction.
It requests labeling for important learning instances among data that hasn't been labeled yet. Subsequently cycles through conducting learning using data rich in information and requesting additional data
- Choose and add the point with the highest uncertainty to the training set (by querying/labeling that point)
- Train on the new training set
- Go to #1 till convergence or budget elapsed
Active Learning Notion
Active Learning Methods
