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What is Bayesian Active Learning with GPs about?

This paper addresses the bias-variance trade-off in active learning using Gaussian Processes (GPs), proposing two new acquisition functions: Bayesian Query-by-Committee (B-QBC) and Query by Mixture of Gaussian Processes (QB-MGP). These functions are designed to optimize the selection of new data points for labeling while minimizing predictive variance, thus improving efficiency in data acquisition. Empirical results demonstrate that these new methods outperform traditional approaches across various benchmar

Author

Han Cao

Language

EN