7 citations · 25 across the 14 of their papers we have counts for
16 papers
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…
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…
Near-Optimal Algorithm for Non-Stationary Kernelized Bandits
Shogo Iwazaki, Shion Takeno
This paper studies a non-stationary kernelized bandit (KB) problem, also called time-varying Bayesian optimization, where one seeks to minimize the regret under an unknown reward f…
Bayesian Optimization for Cascade-type Multi-stage Processes
Shunya Kusakawa, Shion Takeno, Yu Inatsu +6
Complex processes in science and engineering are often formulated as multistage decision-making problems. In this paper, we consider a type of multistage decision-making process ca…
Active learning for distributionally robust level-set estimation
Yu Inatsu, Shogo Iwazaki, Ichiro Takeuchi
Many cases exist in which a black-box function with high evaluation cost depends on two types of variables and , where is a controllable \emph{design} va…
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
Vo Nguyen Le Duy, Shogo Iwazaki, Ichiro Takeuchi
Although a vast body of literature relates to image segmentation methods that use deep neural networks (DNNs), less attention has been paid to assessing the statistical reliability…
Mean-Variance Analysis in Bayesian Optimization under Uncertainty
Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi
We consider active learning (AL) in an uncertain environment in which trade-off between multiple risk measures need to be considered. As an AL problem in such an uncertain environm…
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure
Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi
In many product development problems, the performance of the product is governed by two types of parameters called design parameter and environmental parameter. While the former is…
Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty
Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi
In the manufacturing industry, it is often necessary to repeat expensive operational testing of machine in order to identify the range of input conditions under which the machine o…