Nearly-Optimal Algorithm for Adversarial Kernelized Bandits
Fuente:
arXiv
Saved in:
| Main Author: | Iwazaki, Shogo |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Near-Optimal Algorithm for Non-Stationary Kernelized Bandits
by: Iwazaki, Shogo, et al.
Published: (2024)
by: Iwazaki, Shogo, et al.
Published: (2024)
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)
Tighter Regret Lower Bound for Gaussian Process Bandits with Squared Exponential Kernel in Hypersphere
by: Iwazaki, Shogo
Published: (2026)
by: Iwazaki, Shogo
Published: (2026)
Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance
by: Iwazaki, Shogo, et al.
Published: (2025)
by: Iwazaki, Shogo, et al.
Published: (2025)
Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
by: Iwazaki, Shogo
Published: (2025)
by: Iwazaki, Shogo
Published: (2025)
High-dimensional Nonparametric Contextual Bandit Problem
by: Iwazaki, Shogo, et al.
Published: (2025)
by: Iwazaki, Shogo, et al.
Published: (2025)
Near-Optimal Regret in Adversarial Kernel Bandits
by: Zhang, Yu-Jie, et al.
Published: (2026)
by: Zhang, Yu-Jie, et al.
Published: (2026)
On Regret Bounds of Thompson Sampling for Bayesian Optimization
by: Takeno, Shion, et al.
Published: (2026)
by: Takeno, Shion, et al.
Published: (2026)
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback
by: Di, Qiwei, et al.
Published: (2024)
by: Di, Qiwei, et al.
Published: (2024)
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses
by: Zuo, Shiliang
Published: (2020)
by: Zuo, Shiliang
Published: (2020)
Clus-UCB: A Near-Optimal Algorithm for Clustered Bandits
by: Gore, Aakash, et al.
Published: (2025)
by: Gore, Aakash, et al.
Published: (2025)
Near-Optimal Regret for Distributed Adversarial Bandits: A Black-Box Approach
by: Qiu, Hao, et al.
Published: (2026)
by: Qiu, Hao, et al.
Published: (2026)
A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions
by: Kim, Sanghwa, et al.
Published: (2026)
by: Kim, Sanghwa, et al.
Published: (2026)
Near Optimal Pure Exploration in Logistic Bandits
by: Rivera, Eduardo Ochoa, et al.
Published: (2024)
by: Rivera, Eduardo Ochoa, et al.
Published: (2024)
Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries
by: Maiti, Arnab, et al.
Published: (2025)
by: Maiti, Arnab, et al.
Published: (2025)
Nearly Minimax Optimal Submodular Maximization with Bandit Feedback
by: Tajdini, Artin, et al.
Published: (2023)
by: Tajdini, Artin, et al.
Published: (2023)
Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
by: Lee, Joongkyu, et al.
Published: (2024)
by: Lee, Joongkyu, et al.
Published: (2024)
Dose-finding design based on level set estimation in phase I cancer clinical trials
by: Seno, Keiichiro, et al.
Published: (2025)
by: Seno, Keiichiro, et al.
Published: (2025)
Laplacian Kernelized Bandit
by: Wu, Shuang, et al.
Published: (2026)
by: Wu, Shuang, et al.
Published: (2026)
Optimal and Practical Batched Linear Bandit Algorithm
by: Yu, Sanghoon, et al.
Published: (2025)
by: Yu, Sanghoon, et al.
Published: (2025)
Linear Bandits on Ellipsoids: Minimax Optimal Algorithms
by: Zhang, Raymond, et al.
Published: (2025)
by: Zhang, Raymond, et al.
Published: (2025)
Optimal Streaming Algorithms for Multi-Armed Bandits
by: Jin, Tianyuan, et al.
Published: (2024)
by: Jin, Tianyuan, et al.
Published: (2024)
Nearly Optimal Best Arm Identification for Semiparametric Bandits
by: Kim, Seok-Jin
Published: (2026)
by: Kim, Seok-Jin
Published: (2026)
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback
by: Cassel, Asaf, et al.
Published: (2024)
by: Cassel, Asaf, et al.
Published: (2024)
An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction
by: van Erven, Tim, et al.
Published: (2025)
by: van Erven, Tim, et al.
Published: (2025)
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs
by: Kash, Ian A., et al.
Published: (2022)
by: Kash, Ian A., et al.
Published: (2022)
Neural Risk-sensitive Satisficing in Contextual Bandits
by: Ito, Shogo, et al.
Published: (2025)
by: Ito, Shogo, et al.
Published: (2025)
Near Optimal Best Arm Identification for Clustered Bandits
by: Yash, et al.
Published: (2025)
by: Yash, et al.
Published: (2025)
Constrained Online Two-stage Stochastic Optimization: Near Optimal Algorithms via Adversarial Learning
by: Jiang, Jiashuo
Published: (2023)
by: Jiang, Jiashuo
Published: (2023)
Optimal Arm Elimination Algorithms for Combinatorial Bandits
by: Wen, Yuxiao, et al.
Published: (2025)
by: Wen, Yuxiao, et al.
Published: (2025)
Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms
by: Tatlı, Meltem, et al.
Published: (2025)
by: Tatlı, Meltem, et al.
Published: (2025)
Near-Optimal Primal-Dual Algorithm for Learning Linear Mixture CMDPs with Adversarial Rewards
by: Yu, Kihyun, et al.
Published: (2026)
by: Yu, Kihyun, et al.
Published: (2026)
Nonparametric Kernel Clustering with Bandit Feedback
by: Thuot, Victor, et al.
Published: (2026)
by: Thuot, Victor, et al.
Published: (2026)
Consequences of Kernel Regularity for Bandit Optimization
by: Lee, Madison, et al.
Published: (2025)
by: Lee, Madison, et al.
Published: (2025)
Differentially Private Kernelized Contextual Bandits
by: Pavlovic, Nikola, et al.
Published: (2025)
by: Pavlovic, Nikola, et al.
Published: (2025)
Near-Optimality of Contrastive Divergence Algorithms
by: Glaser, Pierre, et al.
Published: (2025)
by: Glaser, Pierre, et al.
Published: (2025)
Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition
by: Li, Long-Fei, et al.
Published: (2024)
by: Li, Long-Fei, et al.
Published: (2024)
HyperArm Bandit Optimization: A Novel approach to Hyperparameter Optimization and an Analysis of Bandit Algorithms in Stochastic and Adversarial Settings
by: Karroum, Samih, et al.
Published: (2025)
by: Karroum, Samih, et al.
Published: (2025)
A Near-optimal, Scalable and Parallelizable Framework for Stochastic Bandits Robust to Adversarial Corruptions and Beyond
by: Hu, Zicheng, et al.
Published: (2025)
by: Hu, Zicheng, et al.
Published: (2025)
Near-Optimal Regret for Efficient Stochastic Combinatorial Semi-Bandits
by: Ye, Zichun, et al.
Published: (2025)
by: Ye, Zichun, et al.
Published: (2025)
Similar Items
-
Near-Optimal Algorithm for Non-Stationary Kernelized Bandits
by: Iwazaki, Shogo, et al.
Published: (2024) -
Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits
by: Iwazaki, Shogo
Published: (2025) -
Tighter Regret Lower Bound for Gaussian Process Bandits with Squared Exponential Kernel in Hypersphere
by: Iwazaki, Shogo
Published: (2026) -
Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance
by: Iwazaki, Shogo, et al.
Published: (2025) -
Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
by: Iwazaki, Shogo
Published: (2025)