Yu Inatsu

According to our database, Yu Inatsu authored at least 18 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2023

Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds.

Shion Takeno, Yu Inatsu, Masayuki Karasuyama, Ichiro Takeuchi

CoRR, 2023

Randomized Gaussian Process Upper Confidence Bound with Tight Bayesian Regret Bounds.

Shion Takeno, Yu Inatsu, Masayuki Karasuyama

CoRR, 2023

Distributionally Robust Multi-objective Bayesian Optimization under Uncertain Environments.

Yu Inatsu, Ichiro Takeuchi

CoRR, 2023

Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds.

Shion Takeno, Yu Inatsu, Masayuki Karasuyama

Proceedings of the International Conference on Machine Learning, 2023

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

Bayesian Optimization for Distributionally Robust Chance-constrained Problem.

Yu Inatsu, Shion Takeno, Masayuki Karasuyama, Ichiro Takeuchi

Proceedings of the International Conference on Machine Learning, 2022

2021

Bayesian Quadrature Optimization for Probability Threshold Robustness Measure.

Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi

Neural Comput., 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

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

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

Active Learning for Distributionally Robust Level-Set Estimation.

Yu Inatsu, Shogo Iwazaki, Ichiro Takeuchi

Proceedings of the 38th International Conference on Machine Learning, 2021

Mean-Variance Analysis in Bayesian Optimization under Uncertainty.

Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives.

Yu Inatsu, Daisuke Sugita, Kazuaki Toyoura, 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 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

Bayesian Experimental Design for Finding Reliable Level Set Under Input Uncertainty.

Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi

IEEE Access, 2020

Computing Valid P-Values for Image Segmentation by Selective Inference.

Kosuke Tanizaki, Noriaki Hashimoto, Yu Inatsu, Hidekata Hontani, Ichiro Takeuchi

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

Active learning for level set estimation under cost-dependent input uncertainty.

Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, Ichiro Takeuchi

CoRR, 2019