Bayesian bandits for algorithm selection: latent-state modeling and spatial reward structures

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• dc.contributor.author Ernst, Marvin Michel
• dc.contributor.author Gelabert Cortés, Oriol
• dc.contributor.author Vadenja, Melisa
• dc.date.accessioned 2025-11-26T12:28:41Z
• dc.date.available 2025-11-26T12:28:41Z
• dc.date.issued 2025-06-04
• dc.description Treball fi de màster de: Master's Degree in Data Science. Methodology Program. Curs 2024-2025
• dc.description Tutors: David Rossel i Christian Brownlees
• dc.description.abstract This thesis extends the classical Multi-Armed Bandit (MAB) framework to dynamic and spatial environments. In dynamic settings, Bayesian latent-state models with Thompson Sampling and UCB are evaluated for their ability to adapt to non-stationary rewards, with comparisons to simpler autoregressive (AR) models. For spatially structured problems, Gaussian Process (GP) and Lipschitz bandits are used to exploit correlations between arms. Algorithms such as GP-UCB and Zoom-In demonstrate improved learning efficiency. Empirical results highlight the benefits of modeling temporal and spatial structure, while also emphasizing the computational trade-offs compared to classical, more tractable bandit algorithms.
• dc.description.abstract Esta tesis amplía el marco clásico de Multi-Armed Bandit (MAB) a entornos dinámicos y espaciales. En contextos dinámicos, se evalúan modelos bayesianos con estados latentes, combinados con algoritmos clásicos por su capacidad de adaptarse a recompensas no estacionarias, comparándolos con modelos autorregresivos (AR) más simples. Para el caso de estructura espacial, se emplean GP Bandits y Lipschitz Bandits para aprovechar las correlaciones entre brazos. Algoritmos como GP-UCB y Zoom-In demuestran una mayor eficiencia en el aprendizaje en este entorno. Los resultados empíricos resaltan las ventajas de modelar la estructura temporal y espacial, al tiempo que se enfatizan los costes computacionales frente a los algoritmos clásicos más accesibles.
• dc.identifier.uri http://hdl.handle.net/10230/72017
• dc.language.iso eng
• dc.rights This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0
• dc.subject.keyword Multi-armed bandits
• dc.subject.keyword Latent-state models
• dc.subject.keyword Gaussian process bandits
• dc.subject.keyword Modelos de estado latente
• dc.subject.other Treball de fi de màster – Curs 2024-2025
• dc.title Bayesian bandits for algorithm selection: latent-state modeling and spatial reward structures
• dc.type info:eu-repo/semantics/masterThesis