Introduces BOBa, a multi-armed bandit method for scalable surrogate optimization that adaptively allocates inference and evaluations to promising partitions of ultra-large chemical libraries.
Active-learning strategies in computer-assisted drug discovery
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OPAL learns optimal smooth labeling policies from ML uncertainty scores to enable low-variance prediction-assisted inference with finite-sample coverage guarantees.
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Optimized Labeling Resource Allocation for Prediction-Assisted Inference via OPAL
OPAL learns optimal smooth labeling policies from ML uncertainty scores to enable low-variance prediction-assisted inference with finite-sample coverage guarantees.