Multi-fidelity Bayesian Optimization with Max-value Entropy Search. - Shion Takeno; Hitoshi Fukuoka; Yuhki Tsukada; Toshiyuki Koyama; Motoki Shiga; Ichiro Takeuchi; Masayuki Karasuyama (CoRR, 2019)

Multi-objective Bayesian Optimization using Pareto-frontier Entropy. - Shinya Suzuki; Shion Takeno; Tomoyuki Tamura; Kazuki Shitara; Masayuki Karasuyama (ICML, 2020)

Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization. - Shion Takeno; Hitoshi Fukuoka; Yuhki Tsukada; Toshiyuki Koyama; Motoki Shiga; Ichiro Takeuchi; Masayuki Karasuyama (ICML, 2020)

Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling. - Shion Takeno; Yuhki Tsukada; Hitoshi Fukuoka; Toshiyuki Koyama; Motoki Shiga; Masayuki Karasuyama (CoRR, 2020)

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

Sequential and Parallel Constrained Max-value Entropy Search via Information Lower Bound. - Shion Takeno; Tomoyuki Tamura; Kazuki Shitara; Masayuki Karasuyama (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)

A Generalized Framework of Multifidelity Max-Value Entropy Search Through Joint Entropy. - Shion Takeno; Hitoshi Fukuoka; Yuhki Tsukada; Toshiyuki Koyama; Motoki Shiga; Ichiro Takeuchi; Masayuki Karasuyama (Neural Comput., 2022)

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

Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes. - Shion Takeno; Masahiro Nomura; Masayuki Karasuyama (ICML, 2023)

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

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

Multi-Objective Bayesian Optimization with Active Preference Learning. - Ryota Ozaki 003; Kazuki Ishikawa 003; Youhei Kanzaki; Shinya Suzuki; Shion Takeno; Ichiro Takeuchi; Masayuki Karasuyama (CoRR, 2023)

Multi-Objective Bayesian Optimization with Active Preference Learning. - Ryota Ozaki 003; Kazuki Ishikawa 003; Youhei Kanzaki; Shion Takeno; Ichiro Takeuchi; Masayuki Karasuyama (AAAI, 2024)

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)

Hot off the Press: Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes. - Shion Takeno; Masahiro Nomura; Masayuki Karasuyama (GECCO Companion, 2024)

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

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)

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)

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)