Publications (24)
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation
Toshiaki Tsukurimichi, Yu Inatsu, Vo Nguyen Le Duy +1
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.…
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty
Yu Inatsu, Shion Takeno, Hiroyuki Hanada +2
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…
Distributionally Robust Coreset Selection under Covariate Shift
Tomonari Tanaka, Hiroyuki Hanada, Hanting Yang +9
Coreset selection, which involves selecting a small subset from an existing training dataset, is an approach to reducing training data, and various approaches have been proposed fo…
Bayesian Optimization of Robustness Measures under Input Uncertainty: A Randomized Gaussian Process Upper Confidence Bound Approach
Yu Inatsu
Bayesian optimization based on the Gaussian process upper confidence bound (GP-UCB) offers a theoretical guarantee for optimizing black-box functions. In practice, however, black-b…
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…
Computing Valid p-values for Image Segmentation by Selective Inference
Kosuke Tanizaki, Noriaki Hashimoto, Yu Inatsu +2
Image segmentation is one of the most fundamental tasks of computer vision. In many practical applications, it is essential to properly evaluate the reliability of individual segme…
Distributionally Robust Safe Sample Elimination under Covariate Shift
Hiroyuki Hanada, Tatsuya Aoyama, Satoshi Akahane +9
We consider a machine learning setup where one training dataset is used to train multiple models across slightly different data distributions. This occurs when customized models ar…
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama +1
Among various acquisition functions (AFs) in Bayesian optimization (BO), Gaussian process upper confidence bound (GP-UCB) and Thompson sampling (TS) are well-known options with est…
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama
Gaussian process upper confidence bound (GP-UCB) is a theoretically promising approach for black-box optimization; however, the confidence parameter is considerably large in t…
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…
Distributionally Robust Safe Screening
Hiroyuki Hanada, Satoshi Akahane, Tatsuya Aoyama +8
In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method…
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…
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 enumerating local minima based on Gaussian process derivatives
Yu Inatsu, Daisuke Sugita, Kazuaki Toyoura +1
We study active learning (AL) based on Gaussian Processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function. This problem is challenging…
Regret Analysis for Randomized Gaussian Process Upper Confidence Bound
Shion Takeno, Yu Inatsu, Masayuki Karasuyama
Gaussian process upper confidence bound (GP-UCB) is a theoretically established algorithm for Bayesian optimization (BO), where we assume the objective function follows a GP. O…
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…
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…
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…
Distributionally Robust Active Learning for Gaussian Process Regression
Shion Takeno, Yoshito Okura, Yu Inatsu +9
Gaussian process regression (GPR) or kernel ridge regression is a widely used and powerful tool for nonlinear prediction. Therefore, active learning (AL) for GPR, which actively co…
Generalized Kernel Inducing Points by Duality Gap for Dataset Distillation
Tatsuya Aoyama, Hanting Yang, Hiroyuki Hanada +9
We propose Duality Gap KIP (DGKIP), an extension of the Kernel Inducing Points (KIP) method for dataset distillation. While existing dataset distillation methods often rely on bi-l…
Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization
Shion Takeno, Yu Inatsu, Masayuki Karasuyama +1
Bayesian optimization is a powerful tool for optimizing an expensive-to-evaluate black-box function. In particular, the effectiveness of expected improvement (EI) has been demonstr…
Active learning for level set estimation under input uncertainty and its extensions
Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue +1
Testing under what conditions the product satisfies the desired properties is a fundamental problem in manufacturing industry. If the condition and the property are respectively re…
Active Learning for Level Set Estimation Using Randomized Straddle Algorithms
Yu Inatsu, Shion Takeno, Kentaro Kutsukake +1
Level set estimation (LSE), the problem of identifying the set of input points where a function takes value above (or below) a given threshold, is important in practical applicatio…
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…