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Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents; and existing models apply likelihoodbased estimation. In this paper, we present a max-margin supervised topic model for both continuous and categorical response variables. Our approach, the maximum entropy discrimination latent Dirichlet allocation (MedLDA), utilizes the max-margin principle to train supervised topic models and estimate predictive topicdoi:10.1145/1553374.1553535 dblp:conf/icml/ZhuAX09 fatcat:5xmrakcb4rehvlgywmx5jewqvu