5 papers · 1 filter
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…
Learnability in Online Kernel Selection with Memory Constraint via Data-dependent Regret Analysis
Junfan Li, Shizhong Liao
Online kernel selection is a fundamental problem of online kernel methods.In this paper,we study online kernel selection with memory constraint in which the memory of kernel select…
Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound
Yun Liao, Junfan Li, Shizhong Liao +2
In this paper, we study the mistake bound of online kernel learning on a budget. We propose a new budgeted online kernel learning model, called Ahpatron, which significantly improv…
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…
Improved Regret Bounds for Online Kernel Selection under Bandit Feedback
Junfan Li, Shizhong Liao
In this paper, we improve the regret bound for online kernel selection under bandit feedback. Previous algorithm enjoys a $O((\Vert f\Vert^2_{\mathcal{H}_i}+1)K^{\frac{1}{3}}T^{\fr…