7 citations · 25 across the 14 of their papers we have counts for
4 papers · 1 filter
Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization
Shion Takeno, Shogo Iwazaki
This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization. The known regret upper bounds for the widely used GP batched upper confidence bound a…
Nearly Optimal Algorithms for Adversarial Kernelized Bandits
Shogo Iwazaki
This paper studies kernelized bandits (also known as Gaussian process bandits) in an adversarial environment, where the reward functions in a known reproducing kernel Hilbert space…
On Regret Bounds of Thompson Sampling for Bayesian Optimization
Shion Takeno, Shogo Iwazaki
We study a widely used Bayesian optimization method, Gaussian process Thompson sampling (GP-TS), under the assumption that the objective function is a sample path from a GP. Compar…
Tighter Regret Lower Bound for Gaussian Process Bandits with Squared Exponential Kernel in Hypersphere
Shogo Iwazaki
We study an algorithm-independent, worst-case lower bound for the Gaussian process (GP) bandit problem in the frequentist setting, where the reward function is fixed and has a boun…