Meta-LinEXP3: Online-within-Online Learning for Adversarial Linear Contextual Bandits
Meta-learning has emerged as an effective paradigm for transferring knowledge across sequential bandit tasks. While substantial progress has been made for stochastic bandits and non-contextual adversarial bandits, meta-learning for adversarial linear contextual bandits (ALCBs) with random action sets remains largely unexplored. To address this problem, we propose Meta-LinEXP3, an online-within-online algorithm that constructs a predictable task-level prior from completed tasks to guide the inner LinEXP3 learner. For known context distributions, we develop a policy-centered estimator that achieves an intrinsic-dimension $\mathcal{O}(\sqrt{n})$ per-task regret bound. For unknown distributio...
| Authors | Hao Li, Jie Xu, Zheng Xie |
|---|---|
| Primary category | cs.LG |
| Published | 9 September 2026 |
| Last updated at source | 15 September 2026 |
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