Pretrained neural policies replace repeated GP inference and constrained optimization in safe active learning with a single forward pass, yielding large speedups while preserving query quality.
After the specifiedT data points are collected, we use the initial and queried data to fit a GP model with Type II maximum likelihood (optimization: L-BFGS-B algorithm)
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Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies From Simulated Nonparametric Functions
Pretrained neural policies replace repeated GP inference and constrained optimization in safe active learning with a single forward pass, yielding large speedups while preserving query quality.