Saved in:
| Main Authors: | Shibukawa, Yuki, Tanaka, Koichi, Saito, Yuta, Ito, Shinji |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.07005 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
by: Shimizu, Tatsuhiro, et al.
Published: (2024)
by: Shimizu, Tatsuhiro, et al.
Published: (2024)
Influential Bandits: Pulling an Arm May Change the Environment
by: Sato, Ryoma, et al.
Published: (2025)
by: Sato, Ryoma, et al.
Published: (2025)
Bandit Max-Min Fair Allocation
by: Harada, Tsubasa, et al.
Published: (2025)
by: Harada, Tsubasa, et al.
Published: (2025)
Bandit and Delayed Feedback in Online Structured Prediction
by: Shibukawa, Yuki, et al.
Published: (2025)
by: Shibukawa, Yuki, 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)
New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem
by: Ichikawa, Koji, et al.
Published: (2023)
by: Ichikawa, Koji, et al.
Published: (2023)
Multi-Task Combinatorial Bandits for Budget Allocation
by: Ge, Lin, et al.
Published: (2024)
by: Ge, Lin, et al.
Published: (2024)
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits
by: Kato, Masahiro, et al.
Published: (2024)
by: Kato, Masahiro, et al.
Published: (2024)
Heavy-tailed Linear Bandits: Adversarial Robustness, Best-of-both-worlds, and Beyond
by: Zhao, Canzhe, et al.
Published: (2025)
by: Zhao, Canzhe, et al.
Published: (2025)
Replicability is Asymptotically Free in Multi-armed Bandits
by: Komiyama, Junpei, et al.
Published: (2024)
by: Komiyama, Junpei, et al.
Published: (2024)
A Tight Lower Bound for Non-stochastic Multi-armed Bandits with Expert Advice
by: Chase, Zachary, et al.
Published: (2025)
by: Chase, Zachary, et al.
Published: (2025)
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing
by: Mukherjee, Arpan, et al.
Published: (2024)
by: Mukherjee, Arpan, et al.
Published: (2024)
MultiScale Contextual Bandits for Long Term Objectives
by: Rastogi, Richa, et al.
Published: (2025)
by: Rastogi, Richa, et al.
Published: (2025)
A Perturbation Approach to Unconstrained Linear Bandits
by: Jacobsen, Andrew, et al.
Published: (2026)
by: Jacobsen, Andrew, et al.
Published: (2026)
Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction
by: Kiyohara, Haruka, et al.
Published: (2024)
by: Kiyohara, Haruka, et al.
Published: (2024)
Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
by: Lee, Jongyeong, et al.
Published: (2025)
by: Lee, Jongyeong, et al.
Published: (2025)
Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits
by: Nguyen, Nicolas, et al.
Published: (2024)
by: Nguyen, Nicolas, et al.
Published: (2024)
Offline Contextual Bandits in the Presence of New Actions
by: Kishimoto, Ren, et al.
Published: (2026)
by: Kishimoto, Ren, et al.
Published: (2026)
Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback
by: Ito, Shinji, et al.
Published: (2025)
by: Ito, Shinji, et al.
Published: (2025)
Combinatorial Rising Bandits
by: Song, Seockbean, et al.
Published: (2024)
by: Song, Seockbean, et al.
Published: (2024)
Combinatorial Logistic Bandits
by: Liu, Xutong, et al.
Published: (2024)
by: Liu, Xutong, et al.
Published: (2024)
Off-Policy Evaluation for Ranking Policies under Deterministic Logging Policies
by: Tanaka, Koichi, et al.
Published: (2026)
by: Tanaka, Koichi, et al.
Published: (2026)
Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds
by: Lee, Jongyeong, et al.
Published: (2024)
by: Lee, Jongyeong, et al.
Published: (2024)
Data-dependent Bounds with $T$-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching
by: Nguyen, Quan, et al.
Published: (2025)
by: Nguyen, Quan, et al.
Published: (2025)
Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching
by: Kishimoto, Ren, et al.
Published: (2026)
by: Kishimoto, Ren, et al.
Published: (2026)
Off-Policy Learning with Limited Supply
by: Tanaka, Koichi, et al.
Published: (2026)
by: Tanaka, Koichi, et al.
Published: (2026)
Prompt Optimization with Logged Bandit Data
by: Kiyohara, Haruka, et al.
Published: (2025)
by: Kiyohara, Haruka, et al.
Published: (2025)
Does Feedback Help in Bandits with Arm Erasures?
by: Karakas, Merve, et al.
Published: (2025)
by: Karakas, Merve, et al.
Published: (2025)
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)
Constrained Best Arm Identification in Grouped Bandits
by: Dharod, Sahil, et al.
Published: (2024)
by: Dharod, Sahil, et al.
Published: (2024)
Best Arm Identification for Stochastic Rising Bandits
by: Mussi, Marco, et al.
Published: (2023)
by: Mussi, Marco, et al.
Published: (2023)
Best-Arm Identification in Unimodal Bandits
by: Poiani, Riccardo, et al.
Published: (2024)
by: Poiani, Riccardo, et al.
Published: (2024)
Adversarial Combinatorial Bandits with Switching Costs
by: Dong, Yanyan, et al.
Published: (2024)
by: Dong, Yanyan, et al.
Published: (2024)
Oracle-Efficient Combinatorial Semi-Bandits
by: Kim, Jung-hun, et al.
Published: (2025)
by: Kim, Jung-hun, et al.
Published: (2025)
Multi-thresholding Good Arm Identification with Bandit Feedback
by: Jiang, Xuanke, et al.
Published: (2025)
by: Jiang, Xuanke, et al.
Published: (2025)
Combinatorial Bandit Bayesian Optimization for Tensor Outputs
by: Huang, Jingru, et al.
Published: (2026)
by: Huang, Jingru, et al.
Published: (2026)
Efficient Swap Regret Minimization in Combinatorial Bandits
by: Kontogiannis, Andreas, et al.
Published: (2026)
by: Kontogiannis, Andreas, et al.
Published: (2026)
On the Regularity and Fairness of Combinatorial Multi-Armed Bandit
by: Wu, Xiaoyi, et al.
Published: (2025)
by: Wu, Xiaoyi, et al.
Published: (2025)
Offline Learning for Combinatorial Multi-armed Bandits
by: Liu, Xutong, et al.
Published: (2025)
by: Liu, Xutong, et al.
Published: (2025)
Similar Items
-
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
by: Shimizu, Tatsuhiro, et al.
Published: (2024) -
Influential Bandits: Pulling an Arm May Change the Environment
by: Sato, Ryoma, et al.
Published: (2025) -
Bandit Max-Min Fair Allocation
by: Harada, Tsubasa, et al.
Published: (2025) -
Bandit and Delayed Feedback in Online Structured Prediction
by: Shibukawa, Yuki, et al.
Published: (2025) -
Optimal Arm Elimination Algorithms for Combinatorial Bandits
by: Wen, Yuxiao, et al.
Published: (2025)