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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Introduces PK-MIQP, a piecewise-linear kernel approximation that converts Gaussian process acquisition function optimization into a solvable MIQP for any stationary or dot-product kernel, with regret bounds and tests on synthetic and real 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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Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
Introduces PK-MIQP, a piecewise-linear kernel approximation that converts Gaussian process acquisition function optimization into a solvable MIQP for any stationary or dot-product kernel, with regret bounds and tests on synthetic and real tasks.