Multi-Relational Learning at Scale with ADMM

3 Apr 2016Lucas DrumondErnesto Diaz-AvilesLars Schmidt-Thieme

Learning from multiple-relational data which contains noise, ambiguities, or duplicate entities is essential to a wide range of applications such as statistical inference based on Web Linked Data, recommender systems, computational biology, and natural language processing. These tasks usually require working with very large and complex datasets - e.g., the Web graph - however, current approaches to multi-relational learning are not practical for such scenarios due to their high computational complexity and poor scalability on large data... (read more)

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