An LLM-based evolutionary search discovers novel kernels for high-dimensional Bayesian optimization, achieving an average rank of 1.2 out of 17 on five benchmarks via two-stage proposal and LOO-CRPS selection.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
AdaScale-TuRBO scales Gaussian process lengthscales with problem dimension D and trust region side length L to preserve kernel geometry and improve performance over standard TuRBO in high-dimensional settings.
Vanishing gradients from GP initialization explain HDBO failures; MLE of length scales suffices for SOTA performance, enabling the MSR variant on real-world tasks.
citing papers explorer
-
Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization
An LLM-based evolutionary search discovers novel kernels for high-dimensional Bayesian optimization, achieving an average rank of 1.2 out of 17 on five benchmarks via two-stage proposal and LOO-CRPS selection.
-
Rethinking Trust Region Bayesian Optimization in High Dimensions
AdaScale-TuRBO scales Gaussian process lengthscales with problem dimension D and trust region side length L to preserve kernel geometry and improve performance over standard TuRBO in high-dimensional settings.
-
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.