Artificial Constraints and Lipschitz Hints for Unconstrained Online Learning
This work addresses the challenge of parameter-free online learning for researchers and practitioners, offering incremental improvements in regret bounds.
The paper tackles the problem of online convex optimization with Lipschitz losses by providing algorithms that guarantee polynomial regret bounds without prior knowledge of the Lipschitz constant or the norm of the comparison point, improving upon previous exponential penalties.
We provide algorithms that guarantee regret $R_T(u)\le \tilde O(G\|u\|^3 + G(\|u\|+1)\sqrt{T})$ or $R_T(u)\le \tilde O(G\|u\|^3T^{1/3} + GT^{1/3}+ G\|u\|\sqrt{T})$ for online convex optimization with $G$-Lipschitz losses for any comparison point $u$ without prior knowledge of either $G$ or $\|u\|$. Previous algorithms dispense with the $O(\|u\|^3)$ term at the expense of knowledge of one or both of these parameters, while a lower bound shows that some additional penalty term over $G\|u\|\sqrt{T}$ is necessary. Previous penalties were exponential while our bounds are polynomial in all quantities. Further, given a known bound $\|u\|\le D$, our same techniques allow us to design algorithms that adapt optimally to the unknown value of $\|u\|$ without requiring knowledge of $G$.