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Efficient Machine Learning with High Order and Combinatorial Structures

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Two challenges facing machine learning researchers are (a) how to build models that more accurately reflect prior beliefs and constraints relevant to the domain being modeled, and (b) how to model more complexly structured data. In both of these cases, there are tradeoffs between the expressibility of the model and the computational efficiency of learning and inference procedures. In this talk, I will discuss several recent approaches towards building more expressive and more efficient models of structured domains. The focus will be on learning and inference procedures for models that have non-local (high order) and/or combinatorial structure.

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