线性模型加速贝叶斯优化：速度提升百倍到千倍
Fast Bayesian Optimization Through Linear Models
有人把贝叶斯优化提速了 100 到 1000 倍，性能还不掉，6000 多维也能跑，搞超参优化的可以看看。
arXiv 论文从速度-性能帕累托前沿的视角重新审视贝叶斯优化（BO），将其与 CMA-ES 等快速算法对比。作者利用现有 BO 算法中的线性结构，提出一组新的快速 BO 方法，覆盖连续与组合输入，维度从约 10 维到 6000+ 维。这些方法在保持与 SOTA BO 相近性能的同时，速度快 10² 到 10³ 倍，把 BO 拓展到成本更低、更注重速度的问题场景。
Fast Bayesian Optimization Through Linear Models
Bayesian optimization (BO) is typically used only for problems where the underlying function is deemed "expensive" (e.g. in time or money), with the speed of the BO algorithm being of little importance. In the real world, however, problems span a continuous range of values, going from cheap to expensive. In this paper, we present BO algorithms through the lens of a speed-performance Pareto frontier: we compare BO against faster algorithms like CMA-ES, and highlight how certain regions of the Pareto frontier are currently empty. As a solution, we propose a new set of fast BO methods that exploit the linear structure in existing BO algorithms, for both continuous and combinatorial inputs (from ~10 to 6000+ dimensions). Our methods achieve performance similar to state-of-the-art BO, while being more than $10^2\times$ to $10^3\times$ faster, opening the field of BO to an entirely new class of problems.