Group Anomaly Detection using Flexible Genre Models

no code implementations • NeurIPS 2011 • Liang Xiong, Barnabás Póczos, Jeff G. Schneider

We evaluate the effectiveness of FGM on both synthetic and real data sets including images and turbulence data, and show that it is superior to existing approaches in detecting group anomalies.

Group Anomaly Detection

Learning Auto-regressive Models from Sequence and Non-sequence Data

no code implementations • NeurIPS 2011 • Tzu-Kuo Huang, Jeff G. Schneider

Vector Auto-regressive models (VAR) are useful tools for analyzing time series data.

Time Series Time Series Analysis

Learning Multiple Tasks with a Sparse Matrix-Normal Penalty

no code implementations • NeurIPS 2010 • Yi Zhang, Jeff G. Schneider

In this paper, we propose a matrix-variate normal penalty with sparse inverse covariances to couple multiple tasks.

Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text

no code implementations • NeurIPS 2008 • Yi Zhang, Artur Dubrawski, Jeff G. Schneider

In an empirical study, we construct 190 different text classification tasks from a real-world benchmark, and the unlabeled documents are a mixture from all these tasks.

General Classification text-classification +1