55 citations · 200 across the 12 of their papers we have counts for
12 papers
Kernel Conjugate Gradient Methods with Random Projections
Junhong Lin, Volkan Cevher
We propose and study kernel conjugate gradient methods (KCGM) with random projections for least-squares regression over a separable Hilbert space. Considering two types of random p…
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…
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
Junhong Lin, Volkan Cevher
We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We first investigate distrib…
Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces
Junhong Lin, Alessandro Rudi, Lorenzo Rosasco +1
In this paper, we study regression problems over a separable Hilbert space with the square loss, covering non-parametric regression over a reproducing kernel Hilbert space. We inve…
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 of Doubly Stochastic Learning Algorithms
Junhong Lin, Lorenzo Rosasco
Doubly stochastic learning algorithms are scalable kernel methods that perform very well in practice. However, their generalization properties are not well understood and their ana…