Z
Zhao Xu
Researcher at Ludwig Maximilian University of Munich
Publications - 28
Citations - 1140
Zhao Xu is an academic researcher from Ludwig Maximilian University of Munich. The author has contributed to research in topics: Statistical relational learning & Relational model. The author has an hindex of 16, co-authored 26 publications receiving 1040 citations. Previous affiliations of Zhao Xu include Siemens & Tsinghua University.
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Book ChapterDOI
Representative sampling for text classification using support vector machines
TL;DR: A straightforward active learning heuristic, representative sampling, is described, which explores the clustering structure of 'uncertain' documents and identifies the representative samples to query the user opinions, for the purpose of speeding up the convergence of Support Vector Machine (SVM) classifiers.
Proceedings Article
Stochastic Relational Models for Discriminative Link Prediction
TL;DR: A Gaussian process (GP) framework, stochastic relational models (SRM), for learning social, physical, and other relational phenomena where interactions between entities are observed is introduced and extensions of SRM to general relational learning tasks are discussed.
Proceedings Article
Infinite hidden relational models
TL;DR: This paper presents a relational model, which is completely symmetrical, that introduces for each entity (or object) an infinite-dimensional latent variable as part of a Dirichlet process (DP) model, based on a DP Gibbs sampler.
Proceedings ArticleDOI
Robust Online Time Series Prediction with Recurrent Neural Networks
TL;DR: The local features of time series are explored to automatically weight the gradients of the loss of the newly available observations with distributional properties of the data in real time to forecast streaming time series in the presence of anomalies and change points.
Proceedings Article
Multi-relational learning with Gaussian processes
TL;DR: A generalized GP model, named multi-relational Gaussian process model, that is able to deal with an arbitrary number of relations in a domain of interest is presented.