Active Spectral Learning
Project overview
Bayesian machine learning project on active learning for Gaussian Processes with spectral kernels. The project studied how active sampling can improve learning of non-stationary spectral structure.
Motivation
Efficiently learn spectral structure with limited labels and uncertainty-aware query selection.
Method
Gaussian Processes with spectral kernels and active learning criteria driven by predictive uncertainty.
Evaluation
Compared uncertainty-aware sampling against passive baselines on reconstruction and generalization metrics.