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Joint Max Margin and Max Entropy Learning of Graphical Models

Joint Max Margin and Max Entropy Learning of Graphical Models

This video was recorded at NIPS Workshops, Whistler 2009. Inferring structured predictions based on correlated covariates remains a central problem in many fields, including NLP, computer vision, and computational biology. Popular paradigms for training structured input/output models include the maximum (conditional) likelihood estimation, which leads to the well-known CRF; and the max-margin learning, which leads to the structured SVM (a.k.a. M3N), each enjoys some advantages, as well as weaknesses. In this talk, I present a new general framework called Maximum Entropy Discrimination Markov Networks (MEDN), which integrates the margin-based and likelihood-based approaches and combines and extends their merits. This new learning paradigm naturally facilitates integration of the generative and discriminative principles under a unified framework, and the basic strategies can be generalized to learn arbitrary graphical models, such as the generative Bayesian networks or models with structured hidden variables. I will discuss a number of theoretical properties of this model, and show applications of MEDN to learning fully supervised structured i/o model, max-margin structured i/o models with hidden variables, and a max-margin LDA model for jointly discovering discriminative latent topic representations and predicting document label/score of text documents, with compelling performance in each case.


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