14 citations · 17 across the 2 of their papers we have counts for
Showing stat.MLShow all
3 papers · 1 filter
stat.ML2018
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Junhong Lin, Volkan Cevher
We investigate regularized algorithms combining with projection for least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kern…
stat.ML2017★ 3 cited
Optimal Rates for Learning with Nyström Stochastic Gradient Methods
Junhong Lin, Lorenzo Rosasco
In the setting of nonparametric regression, we propose and study a combination of stochastic gradient methods with Nyström subsampling, allowing multiple passes over the data and m…
stat.ML2015★ 14 cited
Iterative Regularization for Learning with Convex Loss Functions
Junhong Lin, Lorenzo Rosasco, Ding-Xuan Zhou
We consider the problem of supervised learning with convex loss functions and propose a new form of iterative regularization based on the subgradient method. Unlike other regulariz…