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arXiv · 2001.10396

Bandit optimisation of functions in the Mat\'ern kernel RKHS

David Janz·David R. Burt·Javier González
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Abstract

We consider the problem of optimising functions in the reproducing kernel Hilbert space (RKHS) of a Mat\'ern kernel with smoothness parameter $\nu$ over the domain $[0,1]^d$ under noisy bandit feedback. Our contribution, the $\pi$-GP-UCB algorithm, is the first practical approach with guaranteed sublinear regret for all $\nu>1$ and $d \geq 1$. Empirical validation suggests better performance and drastically improved computational scalablity compared with its predecessor, Improved GP-UCB.

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David Janz, David R. Burt, Javier González. 2020-01-28. Bandit optimisation of functions in the Mat\'ern kernel RKHS. https://arxiv.org/abs/2001.10396

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