Author

Matthias Mandl

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Preprint Aug 2026

Sharper Regret Bounds for Time-Varying Gaussian Process Bandits with Constant Exploration

Bayesian optimization in a time-varying environment where the unknown reward function evolves according to a Gaussian process drift model is studied, and GP-UCB can be run with a constant exploration parameter and obtained an expected-regret bound whose coefficient depends on the drift rate.

Matthias Mandl, Hanne Kekkonen · 0 citations