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

Quantifying Statistical Significance of Neural Network Representation-Driven Hypotheses by Selective Inference. - Vo Nguyen Le Duy; Shogo Iwazaki; Ichiro Takeuchi (CoRR, 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)

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)

Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference. - Vo Nguyen Le Duy; Shogo Iwazaki; Ichiro Takeuchi (NeurIPS, 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)

Failure-Aware Gaussian Process Optimization with Regret Bounds. - Shogo Iwazaki; Shion Takeno; Tomohiko Tanabe; Mitsuru Irie (NeurIPS, 2023)

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

No-Regret Bandit Exploration based on Soft Tree Ensemble Model. - Shogo Iwazaki; Shinya Suzumura (NeurIPS, 2024)

Near-Optimal Algorithm for Non-Stationary Kernelized Bandits. - Shogo Iwazaki; Shion Takeno (AISTATS, 2025)

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

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance. - Shogo Iwazaki; Shion Takeno (CoRR, 2025)

Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits. - Shogo Iwazaki (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)

High-dimensional Nonparametric Contextual Bandit Problem. - Shogo Iwazaki; Junpei Komiyama; Masaaki Imaizumi (CoRR, 2025)

Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization. - Shogo Iwazaki (CoRR, 2025)