Yu Inatsu

Orcid: 0000-0001-5655-2558

According to our database1, Yu Inatsu authored at least 24 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2024
Regret Analysis for Randomized Gaussian Process Upper Confidence Bound.
CoRR, 2024

Active Learning for Level Set Estimation Using Randomized Straddle Algorithms.
CoRR, 2024

Distributionally Robust Safe Sample Screening.
CoRR, 2024

Distributionally Robust Safe Screening.
CoRR, 2024

Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Randomized Gaussian Process Upper Confidence Bound with Tight Bayesian Regret Bounds.
CoRR, 2023

Distributionally Robust Multi-objective Bayesian Optimization under Uncertain Environments.
CoRR, 2023

Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds.
Proceedings of the International Conference on Machine Learning, 2023

2022
Bayesian Optimization for Cascade-Type Multistage Processes.
Neural Comput., 2022

Bayesian Optimization for Distributionally Robust Chance-constrained Problem.
Proceedings of the International Conference on Machine Learning, 2022

2021
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure.
Neural Comput., 2021

Bayesian Optimization for Cascade-type Multi-stage Processes.
CoRR, 2021

Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference.
CoRR, 2021

Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation.
CoRR, 2021

Active Learning for Distributionally Robust Level-Set Estimation.
Proceedings of the 38th International Conference on Machine Learning, 2021

Mean-Variance Analysis in Bayesian Optimization under Uncertainty.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives.
Neural Comput., 2020

Active Learning for Level Set Estimation Under Input Uncertainty and Its Extensions.
Neural Comput., 2020

Active Learning of Bayesian Linear Models with High-Dimensional Binary Features by Parameter Confidence-Region Estimation.
Neural Comput., 2020

Bayesian Experimental Design for Finding Reliable Level Set Under Input Uncertainty.
IEEE Access, 2020

Computing Valid P-Values for Image Segmentation by Selective Inference.
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.
CoRR, 2019


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