GraphDR-LinUCB projects contextual bandit arms onto a graph's low-frequency eigenspace to obtain the first Õ(k√T) regret bound under approximate smoothness, with a spectral predictor Γ_k that matches outcomes on five of six real datasets.
A Tutorial on Thompson Sampling
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A new algorithm for online influence maximization under a total budget constraint using the independent cascade model and edge-level semi-bandit feedback, with improved regret bounds for both budgeted and cardinality settings.
CSTS learns context-dependent weights for multiple objectives in a multi-objective contextual bandit and outperforms fixed-weight and standard contextual bandit baselines on Swiss public broadcaster programming data.
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
citing papers explorer
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Graph Dimensionality Reduction for Contextual Bandits: Structure-Specific Regret Bounds under Approximate Smoothness and Noisy Eigenspaces
GraphDR-LinUCB projects contextual bandit arms onto a graph's low-frequency eigenspace to obtain the first Õ(k√T) regret bound under approximate smoothness, with a spectral predictor Γ_k that matches outcomes on five of six real datasets.
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Budgeted Online Influence Maximization
A new algorithm for online influence maximization under a total budget constraint using the independent cascade model and edge-level semi-bandit feedback, with improved regret bounds for both budgeted and cardinality settings.
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Contextual Scalarisation Thompson Sampling for multi-objective decisions in public media
CSTS learns context-dependent weights for multiple objectives in a multi-objective contextual bandit and outperforms fixed-weight and standard contextual bandit baselines on Swiss public broadcaster programming data.
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.