78 citations · 88 across the 5 of their papers we have counts for
5 papers
Learning Safely Without Knowing the World:COMPASS-Hedge
Ting Hu, Luanda Cai, Emmanouil-Vasileios Vlatakis-Gkaragkounis
Online learning algorithms often face a fundamental trilemma: balancing regret guarantees between adversarial and stochastic settings and providing baseline safety against a fixed…
Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions
Yunwen Lei, Ting Hu, Guiying Li +1
Stochastic gradient descent (SGD) is a popular and efficient method with wide applications in training deep neural nets and other nonconvex models. While the behavior of SGD is wel…
Learning Rates of Regression with q-norm Loss and Threshold
Ting Hu, Yuan Yao
This paper studies some robust regression problems associated with the -norm loss () and the -insensitive -norm loss in the reproducing kernel Hilbert space. We est…
Consistency Analysis of an Empirical Minimum Error Entropy Algorithm
Jun Fan, Ting Hu, Qiang Wu +1
In this paper we study the consistency of an empirical minimum error entropy (MEE) algorithm in a regression setting. We introduce two types of consistency. The error entropy consi…
Learning Theory Approach to Minimum Error Entropy Criterion
Ting Hu, Jun Fan, Qiang Wu +1
We consider the minimum error entropy (MEE) criterion and an empirical risk minimization learning algorithm in a regression setting. A learning theory approach is presented for thi…