7 citations · 25 across the 14 of their papers we have counts for
5 papers · 1 filter
Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
Shogo Iwazaki
This paper addresses the Bayesian optimization problem (also referred to as the Bayesian setting of the Gaussian process bandit), where the learner seeks to minimize the regret und…
High-dimensional Nonparametric Contextual Bandit Problem
Shogo Iwazaki, Junpei Komiyama, Masaaki Imaizumi
We consider the kernelized contextual bandit problem with a large feature space. This problem involves arms, and the goal of the forecaster is to maximize the cumulative reward…
Dose-finding design based on level set estimation in phase I cancer clinical trials
Keiichiro Seno, Kota Matsui, Shogo Iwazaki +3
The primary objective of phase I cancer clinical trials is to evaluate the safety of a new experimental treatment and to find the maximum tolerated dose (MTD). We show that the MTD…
Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance
Shogo Iwazaki, Shion Takeno
We study the Gaussian process (GP) bandit problem, whose goal is to minimize regret under an unknown reward function lying in some reproducing kernel Hilbert space (RKHS). The maxi…
Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits
Shogo Iwazaki
We study the noise-free Gaussian Process (GP) bandits problem, in which the learner seeks to minimize regret through noise-free observations of the black-box objective function lyi…