Corruption-Robust Contextual Search through Density Updates
Proceedings of Thirty Fifth Conference on Learning Theory, PMLR 178:3504-3505, 2022.
We study the problem of contextual search in the adversarial noise model. Let $d$ be the dimension of the problem, $T$ be the time horizon and $C$ be the total amount of noise in the system. For the $\epsilon$-ball loss, we give a tight regret bound of $O(C + d \log(1/\epsilon))$ improving over the $O(d^3 \log(1/\epsilon)) \log^2(T) + C \log(T) \log(1/\epsilon))$ bound of Krishnamurthy et al (STOC’21). For the symmetric loss, we give an efficient algorithm with regret $O(C+d \log T)$. In terms of techniques, our algorithms are a departure from previous contextual search models in the sense that they keep track of density functions over the candidate vectors instead of a knowledge set consisting of the candidate vectors consistent with the feedback obtained.