Yu Inatsu

CoRR, April, 2025

Distributionally Robust Active Learning for Gaussian Process Regression.

[BibT_eX]

[DOI]

CoRR, February, 2025

Generalized Kernel Inducing Points by Duality Gap for Dataset Distillation.

[BibT_eX]

[DOI]

CoRR, February, 2025

Distributionally Robust Coreset Selection under Covariate Shift.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2025

Bayesian Optimization of Robustness Measures under Input Uncertainty: A Randomized Gaussian Process Upper Confidence Bound Approach.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2025

No-Regret Bayesian Optimization with Stochastic Observation Failures.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Active Learning for Level Set Estimation Using Randomized Straddle Algorithms.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2024

Regret Analysis for Randomized Gaussian Process Upper Confidence Bound.

[BibT_eX]

[DOI]

Shion Takeno

Masayuki Karasuyama

CoRR, 2024

Distributionally Robust Safe Sample Screening.

[BibT_eX]

[DOI]

CoRR, 2024

Distributionally Robust Safe Screening.

[BibT_eX]

[DOI]

CoRR, 2024

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

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

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

[BibT_eX]

[DOI]

Shion Takeno

Masayuki Karasuyama

CoRR, 2023

Distributionally Robust Multi-objective Bayesian Optimization under Uncertain Environments.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

Shion Takeno

Masayuki Karasuyama

Proceedings of the International Conference on Machine Learning, 2023

2022

Bayesian Optimization for Cascade-Type Multistage Processes.

[BibT_eX]

[DOI]

Neural Comput., 2022

Bayesian Optimization for Distributionally Robust Chance-constrained Problem.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

2021

Bayesian Quadrature Optimization for Probability Threshold Robustness Measure.

[BibT_eX]

[DOI]

Neural Comput., 2021

Bayesian Optimization for Cascade-type Multi-stage Processes.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

Toshiaki Tsukurimichi

Vo Nguyen Le Duy

CoRR, 2021

Active Learning for Distributionally Robust Level-Set Estimation.

[BibT_eX]

[DOI]

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

Mean-Variance Analysis in Bayesian Optimization under Uncertainty.

[BibT_eX]

[DOI]

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

2020

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

[BibT_eX]

[DOI]

Neural Comput., 2020

Active Learning for Level Set Estimation Under Input Uncertainty and Its Extensions.

[BibT_eX]

[DOI]

Neural Comput., 2020

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

[BibT_eX]

[DOI]

Neural Comput., 2020

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

[BibT_eX]

[DOI]