A hybrid-feedback Track-and-Stop algorithm for best-arm identification in generalized linear bandits achieves δ-correctness with explicit stopping-time bounds and improved sample efficiency over baselines.
Confidence sequences for generalized linear models via regret analysis
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A sub-Gaussian mixture achieves almost sure ln ln V_T regret on unbounded data via a pathwise bound that holds on the probability-one Ville event.
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Best Arm Identification in Generalized Linear Bandits via Hybrid Feedback
A hybrid-feedback Track-and-Stop algorithm for best-arm identification in generalized linear bandits achieves δ-correctness with explicit stopping-time bounds and improved sample efficiency over baselines.
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Eventually LIL Regret: Almost Sure $\ln\ln T$ Regret for a sub-Gaussian Mixture on Unbounded Data
A sub-Gaussian mixture achieves almost sure ln ln V_T regret on unbounded data via a pathwise bound that holds on the probability-one Ville event.