Bayesian Optimization of Robustness Measures under Input Uncertainty: A Randomized Gaussian Process Upper Confidence Bound Approach
Fuente:
arXiv
Saved in:
| Main Author: | Inatsu, Yu |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Regret Analysis for Randomized Gaussian Process Upper Confidence Bound
by: Takeno, Shion, et al.
Published: (2024)
by: Takeno, Shion, et al.
Published: (2024)
Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
by: Iwazaki, Shogo
Published: (2025)
by: Iwazaki, Shogo
Published: (2025)
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
by: Takeno, Shion, et al.
Published: (2023)
by: Takeno, Shion, et al.
Published: (2023)
Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits
by: Iwazaki, Shogo
Published: (2025)
by: Iwazaki, Shogo
Published: (2025)
Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization
by: Takeno, Shion, et al.
Published: (2025)
by: Takeno, Shion, et al.
Published: (2025)
Wasserstein-type Gaussian Process Regressions for Input Measurement Uncertainty
by: Luo, Hengrui, et al.
Published: (2026)
by: Luo, Hengrui, et al.
Published: (2026)
An Upper Confidence Bound Approach to Estimating the Maximum Mean
by: Kun, Zhang, et al.
Published: (2024)
by: Kun, Zhang, et al.
Published: (2024)
Distributionally Robust Active Learning for Gaussian Process Regression
by: Takeno, Shion, et al.
Published: (2025)
by: Takeno, Shion, et al.
Published: (2025)
Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks
by: Liu, Xingchi, et al.
Published: (2024)
by: Liu, Xingchi, et al.
Published: (2024)
Active Learning for Level Set Estimation Using Randomized Straddle Algorithms
by: Inatsu, Yu, et al.
Published: (2024)
by: Inatsu, Yu, et al.
Published: (2024)
Active learning for level set estimation under input uncertainty and its extensions
by: Inatsu, Yu, et al.
Published: (2019)
by: Inatsu, Yu, et al.
Published: (2019)
Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization
by: Li, Yezhuo, et al.
Published: (2025)
by: Li, Yezhuo, et al.
Published: (2025)
Hierarchical Upper Confidence Bounds for Constrained Online Learning
by: Baheri, Ali
Published: (2024)
by: Baheri, Ali
Published: (2024)
On Improved Regret Bounds In Bayesian Optimization with Gaussian Noise
by: Wang, Jingyi, et al.
Published: (2024)
by: Wang, Jingyi, et al.
Published: (2024)
Inference with the Upper Confidence Bound Algorithm
by: Khamaru, Koulik, et al.
Published: (2024)
by: Khamaru, Koulik, et al.
Published: (2024)
Randomized Confidence Bounds for Stochastic Partial Monitoring
by: Heuillet, Maxime, et al.
Published: (2024)
by: Heuillet, Maxime, et al.
Published: (2024)
Pre-trained Gaussian Processes for Bayesian Optimization
by: Wang, Zi, et al.
Published: (2021)
by: Wang, Zi, et al.
Published: (2021)
Randomized Kriging Believer for Parallel Bayesian Optimization with Regret Bounds
by: Sugiura, Shuhei, et al.
Published: (2026)
by: Sugiura, Shuhei, et al.
Published: (2026)
Causal Bandit Over Unknown Graphs: Upper Confidence Bounds With Backdoor Adjustment
by: Zhao, Yijia, et al.
Published: (2025)
by: Zhao, Yijia, et al.
Published: (2025)
Q-Learning with Shift-Aware Upper Confidence Bound in Non-Stationary Reinforcement Learning
by: Bui, Ha Manh, et al.
Published: (2025)
by: Bui, Ha Manh, et al.
Published: (2025)
Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits
by: Tamás, Ambrus, et al.
Published: (2024)
by: Tamás, Ambrus, et al.
Published: (2024)
Quantum Gaussian Process Regression for Bayesian Optimization
by: Rapp, Frederic, et al.
Published: (2023)
by: Rapp, Frederic, et al.
Published: (2023)
Exact Bayesian Gaussian Cox Processes Using Random Integral
by: Tang, Bingjing, et al.
Published: (2024)
by: Tang, Bingjing, et al.
Published: (2024)
Distributionally Robust Coreset Selection under Covariate Shift
by: Tanaka, Tomonari, et al.
Published: (2025)
by: Tanaka, Tomonari, et al.
Published: (2025)
Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processes
by: Zhang, Yan, et al.
Published: (2026)
by: Zhang, Yan, et al.
Published: (2026)
Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
by: Wei, Yunyue, et al.
Published: (2024)
by: Wei, Yunyue, et al.
Published: (2024)
Upper Counterfactual Confidence Bounds: a New Optimism Principle for Contextual Bandits
by: Xu, Yunbei, et al.
Published: (2020)
by: Xu, Yunbei, et al.
Published: (2020)
Distributionally Robust Safe Sample Elimination under Covariate Shift
by: Hanada, Hiroyuki, et al.
Published: (2024)
by: Hanada, Hiroyuki, et al.
Published: (2024)
Wasserstein Barycenter Gaussian Process based Bayesian Optimization
by: Candelieri, Antonio, et al.
Published: (2025)
by: Candelieri, Antonio, et al.
Published: (2025)
Scalable Bayesian Optimization with Sparse Gaussian Process Models
by: Yang, Ang
Published: (2020)
by: Yang, Ang
Published: (2020)
Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features
by: Caprio, Michele, et al.
Published: (2026)
by: Caprio, Michele, et al.
Published: (2026)
Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation
by: Ha, Huong, et al.
Published: (2023)
by: Ha, Huong, et al.
Published: (2023)
Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization tasks
by: Candelieri, Antonio, et al.
Published: (2026)
by: Candelieri, Antonio, et al.
Published: (2026)
Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Bandits
by: Zhu, Jingxuan, et al.
Published: (2021)
by: Zhu, Jingxuan, et al.
Published: (2021)
Evaluating Uncertainty in Deep Gaussian Processes
by: van der Lende, Matthijs, et al.
Published: (2025)
by: van der Lende, Matthijs, et al.
Published: (2025)
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction
by: Azam, Ubaid, et al.
Published: (2024)
by: Azam, Ubaid, et al.
Published: (2024)
Reliable Abstention under Adversarial Injections: Tight Lower Bounds and New Upper Bounds
by: Edelman, Ezra, et al.
Published: (2026)
by: Edelman, Ezra, et al.
Published: (2026)
Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization
by: Xu, Zhitong, et al.
Published: (2024)
by: Xu, Zhitong, et al.
Published: (2024)
Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
by: Pion, Aurélien, et al.
Published: (2026)
by: Pion, Aurélien, et al.
Published: (2026)
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
by: Tran-The, Hung, et al.
Published: (2022)
by: Tran-The, Hung, et al.
Published: (2022)
Similar Items
-
Regret Analysis for Randomized Gaussian Process Upper Confidence Bound
by: Takeno, Shion, et al.
Published: (2024) -
Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
by: Iwazaki, Shogo
Published: (2025) -
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
by: Takeno, Shion, et al.
Published: (2023) -
Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits
by: Iwazaki, Shogo
Published: (2025) -
Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization
by: Takeno, Shion, et al.
Published: (2025)