14 citations · 17 across the 2 of their papers we have counts for
4 papers
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…
Generalization Properties and Implicit Regularization for Multiple Passes SGM
Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco
We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions. We show that, in the absence of…
Restricted -Isometry Properties Adapted to Frames for Nonconvex -Analysis
Junhong Lin, Song Li
This paper discusses reconstruction of signals from few measurements in the situation that signals are sparse or approximately sparse in terms of a general frame via the -anal…
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…