2 papers
stat.ML2024
Tighter Confidence Bounds for Sequential Kernel Regression
Hamish Flynn, David Reeb
Confidence bounds are an essential tool for rigorously quantifying the uncertainty of predictions. They are a core component in many sequential learning and decision-making algorit…
stat.ML2023
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures
Hamish Flynn, David Reeb, Melih Kandemir +1
We present improved algorithms with worst-case regret guarantees for the stochastic linear bandit problem. The widely used "optimism in the face of uncertainty" principle reduces a…