Threshold-Based Optimal Arm Selection in Monotonic Bandits: Regret Lower Bounds and Algorithms
Fuente:
arXiv
Saved in:
| Main Authors: | Varude, Chanakya, Chaudhary, Jay, Kaushik, Siddharth, Chaporkar, Prasanna |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Clus-UCB: A Near-Optimal Algorithm for Clustered Bandits
by: Gore, Aakash, et al.
Published: (2025)
by: Gore, Aakash, et al.
Published: (2025)
Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms
by: Réveillard, William, et al.
Published: (2025)
by: Réveillard, William, et al.
Published: (2025)
Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms
by: Tatlı, Meltem, et al.
Published: (2025)
by: Tatlı, Meltem, et al.
Published: (2025)
Beyond the Lower Bound: Bridging Regret Minimization and Best Arm Identification in Lexicographic Bandits
by: Xue, Bo, et al.
Published: (2025)
by: Xue, Bo, et al.
Published: (2025)
Variance-Dependent Regret Lower Bounds for Contextual Bandits
by: He, Jiafan, et al.
Published: (2025)
by: He, Jiafan, et al.
Published: (2025)
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)
Tighter Regret Lower Bound for Gaussian Process Bandits with Squared Exponential Kernel in Hypersphere
by: Iwazaki, Shogo
Published: (2026)
by: Iwazaki, Shogo
Published: (2026)
Rising Rested Bandits: Lower Bounds and Efficient Algorithms
by: Fiandri, Marco, et al.
Published: (2024)
by: Fiandri, Marco, et al.
Published: (2024)
Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedback
by: Ba, Wenjia, et al.
Published: (2021)
by: Ba, Wenjia, et al.
Published: (2021)
Regret Tail Characterization of Optimal Bandit Algorithms with Generic Rewards
by: Panda, Subhodip, et al.
Published: (2026)
by: Panda, Subhodip, et al.
Published: (2026)
Asymptotically and Minimax Optimal Regret Bounds for Multi-Armed Bandits with Abstention
by: Yang, Junwen, et al.
Published: (2024)
by: Yang, Junwen, et al.
Published: (2024)
Optimal Regret for Single Index Bandits
by: Dey, Devdan, et al.
Published: (2026)
by: Dey, Devdan, 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)
Improved Regret Bounds for Bandits with Expert Advice
by: Cesa-Bianchi, Nicolò, et al.
Published: (2024)
by: Cesa-Bianchi, Nicolò, et al.
Published: (2024)
Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms
by: Zhang, Qining, et al.
Published: (2023)
by: Zhang, Qining, et al.
Published: (2023)
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)
Optimal Thresholding Linear Bandit
by: Rivera, Eduardo Ochoa, et al.
Published: (2024)
by: Rivera, Eduardo Ochoa, et al.
Published: (2024)
Near-Optimal Regret in Adversarial Kernel Bandits
by: Zhang, Yu-Jie, et al.
Published: (2026)
by: Zhang, Yu-Jie, et al.
Published: (2026)
Optimal Regret for Policy Optimization in Contextual Bandits
by: Levy, Orin, et al.
Published: (2026)
by: Levy, Orin, et al.
Published: (2026)
Queue Length Regret Bounds for Contextual Queueing Bandits
by: Bae, Seoungbin, et al.
Published: (2026)
by: Bae, Seoungbin, et al.
Published: (2026)
Information Capacity Regret Bounds for Bandits with Mediator Feedback
by: Eldowa, Khaled, et al.
Published: (2024)
by: Eldowa, Khaled, et al.
Published: (2024)
EVaR-Optimal Arm Identification in Bandits
by: Ahmadipour, Mehrasa, et al.
Published: (2025)
by: Ahmadipour, Mehrasa, et al.
Published: (2025)
Lasso Bandit with Compatibility Condition on Optimal Arm
by: Lee, Harin, et al.
Published: (2024)
by: Lee, Harin, et al.
Published: (2024)
No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints
by: Losalka, Arpan, et al.
Published: (2024)
by: Losalka, Arpan, et al.
Published: (2024)
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
by: Lee, Junghyun, et al.
Published: (2023)
by: Lee, Junghyun, 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)
Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference
by: Jamshidi, Fateme, et al.
Published: (2025)
by: Jamshidi, Fateme, et al.
Published: (2025)
Near-optimal Per-Action Regret Bounds for Sleeping Bandits
by: Nguyen, Quan, et al.
Published: (2024)
by: Nguyen, Quan, et al.
Published: (2024)
Regret Bounds for Noise-Free Cascaded Kernelized Bandits
by: Li, Zihan, et al.
Published: (2022)
by: Li, Zihan, et al.
Published: (2022)
Online Inverse Linear Optimization: Efficient Logarithmic-Regret Algorithm, Robustness to Suboptimality, and Lower Bound
by: Sakaue, Shinsaku, et al.
Published: (2025)
by: Sakaue, Shinsaku, et al.
Published: (2025)
Nearly Optimal Best Arm Identification for Semiparametric Bandits
by: Kim, Seok-Jin
Published: (2026)
by: Kim, Seok-Jin
Published: (2026)
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)
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback
by: Cassel, Asaf, et al.
Published: (2024)
by: Cassel, Asaf, et al.
Published: (2024)
On the Optimal Regret of Locally Private Linear Contextual Bandit
by: Li, Jiachun, et al.
Published: (2024)
by: Li, Jiachun, et al.
Published: (2024)
Improved Regret Bounds for Linear Bandits with Heavy-Tailed Rewards
by: Tajdini, Artin, et al.
Published: (2025)
by: Tajdini, Artin, et al.
Published: (2025)
Variance-Dependent Regret Bounds for Non-stationary Linear Bandits
by: Wang, Zhiyong, et al.
Published: (2024)
by: Wang, Zhiyong, et al.
Published: (2024)
Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits
by: Di, Qiwei, et al.
Published: (2023)
by: Di, Qiwei, et al.
Published: (2023)
Active Context Selection Improves Simple Regret in Contextual Bandits
by: Shahverdikondori, Mohammad, et al.
Published: (2026)
by: Shahverdikondori, Mohammad, et al.
Published: (2026)
Near Optimal Best Arm Identification for Clustered Bandits
by: Yash, et al.
Published: (2025)
by: Yash, et al.
Published: (2025)
Similar Items
-
Clus-UCB: A Near-Optimal Algorithm for Clustered Bandits
by: Gore, Aakash, et al.
Published: (2025) -
Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms
by: Réveillard, William, et al.
Published: (2025) -
Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms
by: Tatlı, Meltem, et al.
Published: (2025) -
Beyond the Lower Bound: Bridging Regret Minimization and Best Arm Identification in Lexicographic Bandits
by: Xue, Bo, et al.
Published: (2025) -
Variance-Dependent Regret Lower Bounds for Contextual Bandits
by: He, Jiafan, et al.
Published: (2025)