78 citations · 134 across the 6 of their papers we have counts for
6 papers · 1 filter
A Framework of Learning Through Empirical Gain Maximization
Yunlong Feng, Qiang Wu
We develop in this paper a framework of empirical gain maximization (EGM) to address the robust regression problem where heavy-tailed noise or outliers may present in the response…
Optimal Rates of Distributed Regression with Imperfect Kernels
Hongwei Sun, Qiang Wu
Distributed machine learning systems have been receiving increasing attentions for their efficiency to process large scale data. Many distributed frameworks have been proposed for…
Model Adaptation via Model Interpolation and Boosting for Web Search Ranking
Jianfeng Gao, Qiang Wu, Chris Burges +5
This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The resu…
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
Zhengchu Guo, Lei Shi, Qiang Wu
Distributed learning is an effective way to analyze big data. In distributed regression, a typical approach is to divide the big data into multiple blocks, apply a base regression…
A new approach for physiological time series
Dong Mao, Yang Wang, Qiang Wu
We developed a new approach for the analysis of physiological time series. An iterative convolution filter is used to decompose the time series into various components. Statistics…
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…