The 2nd Workshop on Scalable Machine Learning: Theory and Applications

Topics of Interest Distributed data analytics architectures Data separation and integration techniques Machine learning algorithms for GPUs Machine learning algorithms for clouds Machine learning algorithms for clusters Theory and algorithms of data reduction techniques for big data Online/incremental/stochastic learning algorithms Random projection Hashing techniques Data sampling algorithms Theory and algorithms of large-scale matrix approximation Bound analysis of matrix approximation algorithms Distributed matrix factorization Distributed multiway array analysis Online dictionary learning Distributed topic modeling algorithms Heterogeneous learning on big multimodal data Multiview learning Multitask learning Transfer learning Semi-supervised learning Active learning Temporal analysis and spatial analysis in big data Real-time analysis for data stream Trend prediction in financial data Topic detection in instant message systems Real time modeling of events in dynamic networks Spatial modeling on maps Scalable machine learning in large graphs Communities discovery and analysis in social networks Link prediction in networks Anomaly detection in social networks Fusion of information from multiple blogs, rating systems, and social networks Integration of text, videos, images, sounds in social networks Recommender systems Novel applications of scalable machine learning in big data Decision making with big data Counterfactual reasoning with big data Medical/health informatics big data analysis Security big data analysis Astronomy big data analysis Biological big data analysis Urban/smart city big data analysis Education big data analysis