arXiv Machine Learning
Ranking Bandits for Carousel Interfaces with Observable Browsing Depth
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arXiv Machine Learning
Aug 19
Latent Order Bandits
The paper introduces Latent Order Bandits (LOB), a new bandit framework that relaxes the strict assumptions of traditional latent bandits by only requiring a partial order of action preferences within each latent state. LOB allows instances sharing the same state to have different reward distributions as long as the action ranking remains consistent, making it suitable for scenarios like user groups on streaming services who agree on genre preferences but rate differently. The authors present an upper‑confidence bound algorithm for both total and partial latent orders, provide regret bounds, and propose a posterior‑sampling variant that empirically outperforms full‑prior latent bandits when reward scales vary across instances sharing the same latent state.
arXiv Machine Learning
Aug 13
DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks
arXiv:2608. 11873v1 Announce Type: new Abstract: In this work, we extend the Dependent Click Model (DCM) Bandits to a multiplayer information-asymmetric setting, where multiple agents interact with a shared ranked list and may observe multiple clicks per session, introducing new challenges for selection strategies.
arXiv AI
Jun 24
Impatient Bandits: Optimizing for the Long-Term Without Delay
arXiv:2501. 07761v2 Announce Type: replace-cross Abstract: Increasingly, recommender systems are tasked with improving users' long-term satisfaction.
arXiv Machine Learning
3d ago
Empirical Bayesian Multi-Bandit Learning
The paper introduces a hierarchical Bayesian framework for multi-task contextual bandits, capturing both heterogeneity and correlations among bandit instances. It proposes an empirical Bayesian method to estimate the covariance matrix of the prior, leading to two new algorithms—ebmTS and ebmUCB—that incorporate this prior into decision making. The authors provide frequentist regret bounds and show through synthetic and real-world experiments that these algorithms outperform existing methods, especially in complex settings.
arXiv Machine Learning
Aug 4
Characterizing Bias in Post-Bandit Inference under Index Algorithms
arXiv:2608. 01069v1 Announce Type: new Abstract: Bandit algorithms generate data for downstream inference, but adaptive sampling biases post-bandit sample means.
arXiv Machine Learning
Jul 13
Optimal Top-$k$ Identification from Pairwise Comparisons
arXiv:2607. 08979v1 Announce Type: new Abstract: We study the active learning problem of fixed-confidence top-$k$ identification from noisy pairwise comparisons.