arXiv Machine Learning
Jun 4
Offline-to-Online Learning in Linear Bandits
arXiv:2606. 04305v1 Announce Type: new Abstract: We study online learning with an additional offline dataset in the stochastic linear bandit setting.
DAL: A Practical Prior-Free Black-Box Framework for Piecewise Stationary Bandits
arXiv:2501. 19401v5 Announce Type: replace Abstract: We introduce a practical, black-box framework termed Detection Augmented Learning (DAL) for the problem of piecewise stationary bandits without knowledge of the underlying non-stationarity.
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arXiv Machine Learning
Jun 2
Adaptive Exploration for Latent-State Bandits
arXiv:2602. 05139v3 Announce Type: replace Abstract: We study bandits whose rewards depend on an unobserved Markov state that evolves independently of the learner's actions.
arXiv Machine Learning
Jun 24
Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits
arXiv:2606. 23933v1 Announce Type: cross Abstract: We study non-stationary linear contextual bandits where the reward model drifts over time, rendering classical contextual bandit algorithms brittle because historical data becomes systematically biased.
arXiv Machine Learning
2d ago
Rising Multi-Armed Bandits with Known Horizons
arXiv:2602.10727v3 Announce Type: replace Abstract: Rising Multi-Armed Bandits (RMABs) model sequential decision problems where each arm's expected reward improves with repeated pulls. In such proble...
arXiv Machine Learning
Jun 9
Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits
arXiv:2606. 08977v1 Announce Type: new Abstract: Motivated by the recency effect in online learning, we study algorithms for single-pass *sliding-window streaming multi-armed bandits (MABs)* in this paper.
arXiv Machine Learning
Aug 12
Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits
arXiv:2307. 03587v4 Announce Type: replace Abstract: In non-stationary linear contextual bandits, existing efficient algorithms typically rely on the Weighted Regularized Least-Squares (WRLS) estimator.
arXiv AI
Jun 9
Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts
arXiv:2606. 09802v1 Announce Type: cross Abstract: We consider a variant of the linear contextual stochastic multi-armed bandits, where the learner must provide recommendations to a group of users, each having its personalized preference vector, and in the presence of context distributions that are drifting over time.
arXiv Machine Learning
Jun 9
Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees
arXiv:2606. 09002v1 Announce Type: cross Abstract: We study a stochastic multi-armed bandit problem in which the set of available arms expands over time.
arXiv Machine Learning
Aug 10
Bootstrap-Conditioned Action Selection with Tabular Foundation Models
arXiv:2608. 06559v1 Announce Type: new Abstract: Contextual bandits offer a natural framework for sample-efficient personalization, but practical deployment remains difficult under sparse, biased interaction data, unreliable uncertainty estimates, and severe cold starts.
arXiv Machine Learning
Sep 10
Best-of-Both Worlds for linear contextual bandits with paid observations
arXiv:2510. 07424v3 Announce Type: replace Abstract: We study linear contextual bandits with paid observations, where at each round the learner observes a context, selects an action, and may pay a fixed cost to observe feedback from a subset of arms.
arXiv Machine Learning
Jun 9
Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling
arXiv:2502. 01226v4 Announce Type: replace Abstract: Gaussian process (GP) bandits provide a powerful framework for performing blackbox optimization of unknown functions.
arXiv Machine Learning
Sep 16
Meta-LinEXP3: Online-within-Online Learning for Adversarial Linear Contextual Bandits
Meta-LinEXP3 is an online-within-online algorithm designed for adversarial linear contextual bandits with random action sets. It builds a task-level prior from completed tasks to guide an inner LinEXP3 learner, achieving an σO(√n) per‑task regret when context distributions are known and an σO(n^{2/3}) regret with a past‑only regularized moment estimator when they are unknown. The paper also links prior accuracy to transfer regret, showing that better priors yield sublinear, transfer‑dependent regret across tasks, and demonstrates the method on structured hyperspectral tensor sampling.