78 citations · 412 across the 48 of their papers we have counts for
6 papers · 1 filter
Modified Fejér sequences and applications
Junhong Lin, Lorenzo Rosasco, Silvia Villa +1
In this note, we propose and study the notion of modified Fejér sequences. Within a Hilbert space setting, we show that it provides a unifying framework to prove convergence rates…
On the Robustness of Regularized Pairwise Learning Methods Based on Kernels
Andreas Christmann, Ding-Xuan Zhou
Regularized empirical risk minimization including support vector machines plays an important role in machine learning theory. In this paper regularized pairwise learning (RPL) meth…
Unregularized Online Learning Algorithms with General Loss Functions
Yiming Ying, Ding-Xuan Zhou
In this paper, we consider unregularized online learning algorithms in a Reproducing Kernel Hilbert Spaces (RKHS). Firstly, we derive explicit convergence rates of the unregularize…
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…
Minimax Optimal Rates of Estimation in High Dimensional Additive Models: Universal Phase Transition
Ming Yuan, Ding-Xuan Zhou
We establish minimax optimal rates of convergence for estimation in a high dimensional additive model assuming that it is approximately sparse. Our results reveal an interesting ph…
Online Pairwise Learning Algorithms with Kernels
Yiming Ying, Ding-Xuan Zhou
Pairwise learning usually refers to a learning task which involves a loss function depending on pairs of examples, among which most notable ones include ranking, metric learning an…