A delayed, angle-estimation based LinUCB algorithm achieves \tilde O(sqrt T) strategic regret in a generalized principal-agent model with private types and non-myopic agents.
Forn∈ [nk ·(⌈logn k⌉2 + 1)], we denote byR2,k,n the total regret that arises from the firstnrounds of the pessimistic- optimistic planning stage
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Learning to Lead: Incentivizing Strategic Agents in the Dark
A delayed, angle-estimation based LinUCB algorithm achieves \tilde O(sqrt T) strategic regret in a generalized principal-agent model with private types and non-myopic agents.