2 papers
cs.LG2025
A Polynomial-time Algorithm for Online Sparse Linear Regression with Improved Regret Bound under Weaker Conditions
Junfan Li, Shizhong Liao, Zenglin Xu +1
In this paper, we study the problem of online sparse linear regression (OSLR) where the algorithms are restricted to accessing only out of attributes per instance for predi…
cs.LG2023
Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression
Junfan Li, Shizhong Liao
The trade-off between regret and computational cost is a fundamental problem for online kernel regression, and previous algorithms worked on the trade-off can not keep optimal regr…