Active learning for level set estimation under cost-dependent input uncertainty. - Yu Inatsu; Masayuki Karasuyama; Keiichi Inoue; Ichiro Takeuchi (CoRR, 2019)

Computing Valid P-Values for Image Segmentation by Selective Inference. - Kosuke Tanizaki; Noriaki Hashimoto; Yu Inatsu; Hidekata Hontani; Ichiro Takeuchi (CVPR, 2020)

Bayesian Experimental Design for Finding Reliable Level Set Under Input Uncertainty. - Shogo Iwazaki; Yu Inatsu; Ichiro Takeuchi (IEEE Access, 2020)

Active Learning of Bayesian Linear Models with High-Dimensional Binary Features by Parameter Confidence-Region Estimation. - Yu Inatsu; Masayuki Karasuyama; Keiichi Inoue; Hideki Kandori; Ichiro Takeuchi (Neural Comput., 2020)

Active Learning for Level Set Estimation Under Input Uncertainty and Its Extensions. - Yu Inatsu; Masayuki Karasuyama; Keiichi Inoue; Ichiro Takeuchi (Neural Comput., 2020)

Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives. - Yu Inatsu; Daisuke Sugita; Kazuaki Toyoura; Ichiro Takeuchi (Neural Comput., 2020)

Mean-Variance Analysis in Bayesian Optimization under Uncertainty. - Shogo Iwazaki; Yu Inatsu; Ichiro Takeuchi (AISTATS, 2021)

Active Learning for Distributionally Robust Level-Set Estimation. - Yu Inatsu; Shogo Iwazaki; Ichiro Takeuchi (ICML, 2021)

Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation. - Toshiaki Tsukurimichi; Yu Inatsu; Vo Nguyen Le Duy; Ichiro Takeuchi (CoRR, 2021)

Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference. - Ryota Sugiyama; Hiroki Toda; Vo Nguyen Le Duy; Yu Inatsu; Ichiro Takeuchi (CoRR, 2021)

Bayesian Optimization for Cascade-type Multi-stage Processes. - Shunya Kusakawa; Shion Takeno; Yu Inatsu; Kentaro Kutsukake; Shogo Iwazaki; Takashi Nakano; Toru Ujihara; Masayuki Karasuyama; Ichiro Takeuchi (CoRR, 2021)

Bayesian Quadrature Optimization for Probability Threshold Robustness Measure. - Shogo Iwazaki; Yu Inatsu; Ichiro Takeuchi (Neural Comput., 2021)

Bayesian Optimization for Distributionally Robust Chance-constrained Problem. - Yu Inatsu; Shion Takeno; Masayuki Karasuyama; Ichiro Takeuchi (ICML, 2022)

Bayesian Optimization for Cascade-Type Multistage Processes. - Shunya Kusakawa; Shion Takeno; Yu Inatsu; Kentaro Kutsukake; Shogo Iwazaki; Takashi Nakano; Toru Ujihara; Masayuki Karasuyama; Ichiro Takeuchi (Neural Comput., 2022)

Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds. - Shion Takeno; Yu Inatsu; Masayuki Karasuyama (ICML, 2023)

Distributionally Robust Multi-objective Bayesian Optimization under Uncertain Environments. - Yu Inatsu; Ichiro Takeuchi (CoRR, 2023)

Randomized Gaussian Process Upper Confidence Bound with Tight Bayesian Regret Bounds. - Shion Takeno; Yu Inatsu; Masayuki Karasuyama (CoRR, 2023)

Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty. - Yu Inatsu; Shion Takeno; Hiroyuki Hanada; Kazuki Iwata; Ichiro Takeuchi (AISTATS, 2024)

Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum. - Shogo Iwazaki; Tomohiko Tanabe; Mitsuru Irie; Shion Takeno; Yu Inatsu (AISTATS, 2024)

Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds. - Shion Takeno; Yu Inatsu; Masayuki Karasuyama; Ichiro Takeuchi (ICML, 2024)

Distributionally Robust Safe Screening. - Hiroyuki Hanada; Satoshi Akahane; Tatsuya Aoyama; Tomonari Tanaka; Yoshito Okura; Yu Inatsu; Noriaki Hashimoto; Taro Murayama; Hanju Lee; Shinya Kojima; Ichiro Takeuchi (CoRR, 2024)

Distributionally Robust Safe Sample Screening. - Hiroyuki Hanada; Tatsuya Aoyama; Satoshi Akahane; Tomonari Tanaka; Yoshito Okura; Yu Inatsu; Noriaki Hashimoto; Shion Takeno; Taro Murayama; Hanju Lee; Shinya Kojima; Ichiro Takeuchi (CoRR, 2024)

Regret Analysis for Randomized Gaussian Process Upper Confidence Bound. - Shion Takeno; Yu Inatsu; Masayuki Karasuyama (CoRR, 2024)

Active Learning for Level Set Estimation Using Randomized Straddle Algorithms. - Yu Inatsu; Shion Takeno; Kentaro Kutsukake; Ichiro Takeuchi (Trans. Mach. Learn. Res., 2024)

No-Regret Bayesian Optimization with Stochastic Observation Failures. - Shogo Iwazaki; Tomohiko Tanabe; Mitsuru Irie; Shion Takeno; Kota Matsui; Yu Inatsu (AISTATS, 2025)

Bayesian Optimization of Robustness Measures under Input Uncertainty: A Randomized Gaussian Process Upper Confidence Bound Approach. - Yu Inatsu (Trans. Mach. Learn. Res., 2025)

Distributionally Robust Coreset Selection under Covariate Shift. - Tomonari Tanaka; Hiroyuki Hanada; Hanting Yang; Tatsuya Aoyama; Yu Inatsu; Satoshi Akahane; Yoshito Okura; Noriaki Hashimoto; Taro Murayama; Hanju Lee; Shinya Kojima; Ichiro Takeuchi (Trans. Mach. Learn. Res., 2025)

Generalized Kernel Inducing Points by Duality Gap for Dataset Distillation. - Tatsuya Aoyama; Hanting Yang; Hiroyuki Hanada; Satoshi Akahane; Tomonari Tanaka; Yoshito Okura; Yu Inatsu; Noriaki Hashimoto; Taro Murayama; Hanju Lee; Shinya Kojima; Ichiro Takeuchi (CoRR, 2025)

Distributionally Robust Active Learning for Gaussian Process Regression. - Shion Takeno; Yoshito Okura; Yu Inatsu; Tatsuya Aoyama; Tomonari Tanaka; Satoshi Akahane; Hiroyuki Hanada; Noriaki Hashimoto; Taro Murayama; Hanju Lee; Shinya Kojima; Ichiro Takeuchi (CoRR, 2025)

Bayesian Optimization of Robustness Measures Using Randomized GP-UCB-based Algorithms under Input Uncertainty. - Yu Inatsu (CoRR, 2025)

Dose-finding design based on level set estimation in phase I cancer clinical trials. - Keiichiro Seno; Kota Matsui; Shogo Iwazaki; Yu Inatsu; Shion Takeno; Shigeyuki Matsui (CoRR, 2025)

Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization. - Shion Takeno; Yu Inatsu; Masayuki Karasuyama; Ichiro Takeuchi (CoRR, 2025)