Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2022 | Annual Conference on Computational Learning Theory | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| renato paes | 1 | 331 | 36.45 |
| Chara Podimata | 2 | 0 | 0.68 |
| Jon Schneider | 3 | 3 | 5.06 |