EARL-BO uses RL with an Attention-DeepSets encoder and end-to-end on-policy multi-task fine-tuning to approximate near-optimal multi-step lookahead policies for high-dimensional black-box optimization.
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Vanishing gradients from GP initialization explain HDBO failures; MLE of length scales suffices for SOTA performance, enabling the MSR variant on real-world tasks.
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EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
EARL-BO uses RL with an Attention-DeepSets encoder and end-to-end on-policy multi-task fine-tuning to approximate near-optimal multi-step lookahead policies for high-dimensional black-box optimization.
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Understanding High-Dimensional Bayesian Optimization
Vanishing gradients from GP initialization explain HDBO failures; MLE of length scales suffices for SOTA performance, enabling the MSR variant on real-world tasks.