In many modern problems across areas such as natural language processing, computer vision, and social media inference, one is often interested in learning a Sparse Structured Input-Output Model (SIOM), in which the input variables of the model such as lexicons in a document bear rich structures due to the syntactic and semantic dependences between them in the text; and the output variables such as the elements in a multi-way classification, a parse, or a topic representation are also structured because of their interrelatedness. A SIOM can nicely capture rich structural properties in the data and in the problem, but it also raises severe computational and theoretical challenge on sparse, consistent, and tractable model identification and inference.
In this talk, I will present models, algorithms, and theories that learn Sparse SIOMs of various kinds in very high dimensional input/output space, with fast and highly scalable optimization procedures, and strong statistical guarantees. I will demonstrate application of our approach to problems in large-scale image/text classification, topic modeling, and genomic association analysis.