7 citations · 18 across the 7 of their papers we have counts for
8 papers
Bayesian Optimization for Distributionally Robust Chance-constrained Problem
Yu Inatsu, Shion Takeno, Masayuki Karasuyama +1
In black-box function optimization, we need to consider not only controllable design variables but also uncontrollable stochastic environment variables. In such cases, it is necess…
Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference
Ryota Sugiyama, Hiroki Toda, Vo Nguyen Le Duy +2
In this paper, we study statistical inference of change-points (CPs) in multi-dimensional sequence. In CP detection from a multi-dimensional sequence, it is often desirable not onl…
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…
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…