arXiv:2606. 08410v1 Announce Type: cross Abstract: Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives.
By Linfeng Cao, Ming Shi, Ness B. Shroff
arXiv:2607. 09015v1 Announce Type: cross Abstract: We study contextual bandit problems with correlated arms and access to surrogate reward signals produced by a machine learning model, motivated by applications such as large language model (LLM) routing.
By Ajay Narayanan Sridhar, Ronak Singh, Mehrdad Mahdavi, Vijaykrishnan Narayanan
arXiv:2608. 06750v1 Announce Type: cross Abstract: Iterative refinement has significantly enhanced Large Language Model (LLM) performance; however, existing methods ranging from feedback-based Self-Refine to traditional bandit approaches often rely on static options or overlook the saturation effect.
By Shion Ishikawa, Pablo Loyola, Young-joo Chung, Yun Ching Liu
The paper investigates preference-based bandits where a learner selects pairs of arms and receives binary preference feedback modeled by Bradley–Terry. It introduces the locally sensitive eluder dimension, a new complexity measure for logistic preference feedback, and proposes the GINOP algorithm that uses log-loss confidence sets to balance optimism and exploration. The authors prove a first-order regret bound showing that learning with preference feedback can be as statistically efficient as learning from direct rewards, and they validate their theory with empirical experiments.
By Ahmed Ben Yahmed (CREST, ENSAE Paris, FAIRPLAY), Marc Abeille (FAIRPLAY), Cl\'ement Calauz\`enes (FAIRPLAY)
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.
By Devansh Gupta, Shiv Tavker, Dmitry Efimov, Suchitra Sathyanarayana, Gitanjali Bhutani, Boris N. Oreshkin
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.
By Xia Jiang, Rong J. B. Zhu