Guardado en:
| Autores principales: | Zhang, Yu-Jie, Xu, Sheng-An, Zhao, Peng, Sugiyama, Masashi |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.11847 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update
por: Wang, Jing, et al.
Publicado: (2025)
por: Wang, Jing, et al.
Publicado: (2025)
Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability
por: Zhang, Yu-Jie, et al.
Publicado: (2025)
por: Zhang, Yu-Jie, et al.
Publicado: (2025)
Near-Optimal Regret in Adversarial Kernel Bandits
por: Zhang, Yu-Jie, et al.
Publicado: (2026)
por: Zhang, Yu-Jie, et al.
Publicado: (2026)
Multi-Player Approaches for Dueling Bandits
por: Raveh, Or, et al.
Publicado: (2024)
por: Raveh, Or, et al.
Publicado: (2024)
The Survival Bandit Problem
por: Riou, Charles, et al.
Publicado: (2022)
por: Riou, Charles, et al.
Publicado: (2022)
A Fast Algorithm for the Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit
por: Nakamura, Shintaro, et al.
Publicado: (2023)
por: Nakamura, Shintaro, et al.
Publicado: (2023)
On the Optimal Regret of Locally Private Linear Contextual Bandit
por: Li, Jiachun, et al.
Publicado: (2024)
por: Li, Jiachun, et al.
Publicado: (2024)
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation
por: Zhang, Yu-Jie, et al.
Publicado: (2023)
por: Zhang, Yu-Jie, et al.
Publicado: (2023)
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback
por: Cassel, Asaf, et al.
Publicado: (2024)
por: Cassel, Asaf, et al.
Publicado: (2024)
Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
por: Yu, Sanghoon, et al.
Publicado: (2026)
por: Yu, Sanghoon, et al.
Publicado: (2026)
Optimal Regret for Single Index Bandits
por: Dey, Devdan, et al.
Publicado: (2026)
por: Dey, Devdan, et al.
Publicado: (2026)
Revisiting Matrix Sketching in Linear Bandits: Achieving Sublinear Regret via Dyadic Block Sketching
por: Wen, Dongxie, et al.
Publicado: (2024)
por: Wen, Dongxie, et al.
Publicado: (2024)
No-Regret Linear Bandits under Gap-Adjusted Misspecification
por: Liu, Chong, et al.
Publicado: (2025)
por: Liu, Chong, et al.
Publicado: (2025)
Prior Diffusiveness and Regret in the Linear-Gaussian Bandit
por: Zhu, Yifan, et al.
Publicado: (2026)
por: Zhu, Yifan, et al.
Publicado: (2026)
Optimal Regret for Policy Optimization in Contextual Bandits
por: Levy, Orin, et al.
Publicado: (2026)
por: Levy, Orin, et al.
Publicado: (2026)
Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs
por: Li, Long-Fei, et al.
Publicado: (2024)
por: Li, Long-Fei, et al.
Publicado: (2024)
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates
por: Braun, Guillaume, et al.
Publicado: (2024)
por: Braun, Guillaume, et al.
Publicado: (2024)
Regret Tail Characterization of Optimal Bandit Algorithms with Generic Rewards
por: Panda, Subhodip, et al.
Publicado: (2026)
por: Panda, Subhodip, et al.
Publicado: (2026)
Achieving Optimal Static and Dynamic Regret Simultaneously in Bandits with Deterministic Losses
por: Qian, Jian, et al.
Publicado: (2026)
por: Qian, Jian, et al.
Publicado: (2026)
Parameter-Free Dynamic Regret for Unconstrained Linear Bandits
por: Rumi, Alberto, et al.
Publicado: (2026)
por: Rumi, Alberto, et al.
Publicado: (2026)
Near-Optimal Regret for KL-Regularized Multi-Armed Bandits
por: Ji, Kaixuan, et al.
Publicado: (2026)
por: Ji, Kaixuan, et al.
Publicado: (2026)
Tight Gap-Dependent Memory-Regret Trade-Off for Single-Pass Streaming Stochastic Multi-Armed Bandits
por: Ye, Zichun, et al.
Publicado: (2025)
por: Ye, Zichun, et al.
Publicado: (2025)
Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
por: Lee, Joongkyu, et al.
Publicado: (2024)
por: Lee, Joongkyu, et al.
Publicado: (2024)
Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits
por: Hou, Yunlong, et al.
Publicado: (2024)
por: Hou, Yunlong, et al.
Publicado: (2024)
One Good Source is All You Need: Near-Optimal Regret for Bandits under Heterogeneous Noise
por: Bhat, Amith, et al.
Publicado: (2026)
por: Bhat, Amith, et al.
Publicado: (2026)
Optimal Batched Linear Bandits
por: Ren, Xuanfei, et al.
Publicado: (2024)
por: Ren, Xuanfei, et al.
Publicado: (2024)
No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization
por: Bernasconi, Martino, et al.
Publicado: (2024)
por: Bernasconi, Martino, et al.
Publicado: (2024)
Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms
por: Réveillard, William, et al.
Publicado: (2025)
por: Réveillard, William, et al.
Publicado: (2025)
Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms
por: Tatlı, Meltem, et al.
Publicado: (2025)
por: Tatlı, Meltem, et al.
Publicado: (2025)
Near-Optimal Regret for Distributed Adversarial Bandits: A Black-Box Approach
por: Qiu, Hao, et al.
Publicado: (2026)
por: Qiu, Hao, et al.
Publicado: (2026)
Optimal and Practical Batched Linear Bandit Algorithm
por: Yu, Sanghoon, et al.
Publicado: (2025)
por: Yu, Sanghoon, et al.
Publicado: (2025)
Model Predictive Control is Almost Optimal for Restless Bandit
por: Gast, Nicolas, et al.
Publicado: (2024)
por: Gast, Nicolas, et al.
Publicado: (2024)
Improved Regret Bounds for Linear Bandits with Heavy-Tailed Rewards
por: Tajdini, Artin, et al.
Publicado: (2025)
por: Tajdini, Artin, et al.
Publicado: (2025)
Variance-Dependent Regret Bounds for Non-stationary Linear Bandits
por: Wang, Zhiyong, et al.
Publicado: (2024)
por: Wang, Zhiyong, et al.
Publicado: (2024)
Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost
por: Zheng, Zhong, et al.
Publicado: (2024)
por: Zheng, Zhong, et al.
Publicado: (2024)
On Symmetric Losses for Robust Policy Optimization with Noisy Preferences
por: Nishimori, Soichiro, et al.
Publicado: (2025)
por: Nishimori, Soichiro, et al.
Publicado: (2025)
Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms
por: Tatlı, Meltem, et al.
Publicado: (2025)
por: Tatlı, Meltem, et al.
Publicado: (2025)
Learning What to Recommend: Minimax Optimal Simple Regret in Logistic Bandits
por: Liu, Shuai, et al.
Publicado: (2026)
por: Liu, Shuai, et al.
Publicado: (2026)
Optimal Thresholding Linear Bandit
por: Rivera, Eduardo Ochoa, et al.
Publicado: (2024)
por: Rivera, Eduardo Ochoa, et al.
Publicado: (2024)
Enriching Disentanglement: From Logical Definitions to Quantitative Metrics
por: Zhang, Yivan, et al.
Publicado: (2023)
por: Zhang, Yivan, et al.
Publicado: (2023)
Ejemplares similares
-
Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update
por: Wang, Jing, et al.
Publicado: (2025) -
Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability
por: Zhang, Yu-Jie, et al.
Publicado: (2025) -
Near-Optimal Regret in Adversarial Kernel Bandits
por: Zhang, Yu-Jie, et al.
Publicado: (2026) -
Multi-Player Approaches for Dueling Bandits
por: Raveh, Or, et al.
Publicado: (2024) -
The Survival Bandit Problem
por: Riou, Charles, et al.
Publicado: (2022)