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Found 14 papers, 0 papers with code
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
We show that PIMS achieves the tighter BCR bound and avoids the hyperparameter tuning, unlike GP-UCB.
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
Gaussian process upper confidence bound (GP-UCB) is a theoretically promising approach for black-box optimization; however, the confidence parameter $\beta$ is considerably large in the theorem and chosen heuristically in practice.
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty
In this study, we propose a novel multi-objective Bayesian optimization (MOBO) method to efficiently identify the Pareto front (PF) defined by risk measures for black-box functions under the presence of input uncertainty (IU).
Bayesian Optimization for Distributionally Robust Chance-constrained Problem
In black-box function optimization, we need to consider not only controllable design variables but also uncontrollable stochastic environment variables.
Bayesian Optimization for Cascade-type Multi-stage Processes
A cascade process is a multistage process in which the output of one stage is used as an input for the subsequent stage.
Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference
In this paper, we study statistical inference of change-points (CPs) in multi-dimensional sequence.
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation
In practical data analysis under noisy environment, it is common to first use robust methods to identify outliers, and then to conduct further analysis after removing the outliers.
Active learning for distributionally robust level-set estimation
A natural measure of robustness is the probability that $f(\bm x, \bm w)$ exceeds a given threshold $h$, which is known as the \emph{probability threshold robustness} (PTR) measure in the literature on robust optimization.
Mean-Variance Analysis in Bayesian Optimization under Uncertainty
As an AL problem in such an uncertain environment, we study Mean-Variance Analysis in Bayesian Optimization (MVA-BO) setting.
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure
In many product development problems, the performance of the product is governed by two types of parameters called design parameter and environmental parameter.
Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty
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 operates properly.
Active learning for level set estimation under cost-dependent input uncertainty
As part of a quality control process in manufacturing it is often necessary to test whether all parts of a product satisfy a required property, with as few inspections as possible.
Computing Valid p-values for Image Segmentation by Selective Inference
To overcome this difficulty, we introduce a statistical approach called selective inference, and develop a framework to compute valid p-values in which the segmentation bias is properly accounted for.
Active learning for enumerating local minima based on Gaussian process derivatives
We study active learning (AL) based on Gaussian Processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function.
