Signature transforms approximate path-dependent nonlinear rewards as linear functionals, enabling the DisSigUCB algorithm with a high-probability regret bound of order O(sqrt((d+m)KT)).
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Signature Approach for Contextual Bandits with Nonlinear and Path-dependent Rewards
Signature transforms approximate path-dependent nonlinear rewards as linear functionals, enabling the DisSigUCB algorithm with a high-probability regret bound of order O(sqrt((d+m)KT)).