BAxUS adapts Bayesian optimization over nested random subspaces to achieve better performance than prior high-dimensional methods while providing theoretical guarantees against failure.
Semi-supervised E mbedding L earning for H igh-dimensional B ayesian O ptimization
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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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Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
BAxUS adapts Bayesian optimization over nested random subspaces to achieve better performance than prior high-dimensional methods while providing theoretical guarantees against failure.
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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.