Learning Latent Groups with Hinge-loss Markov Random Fields

  title={Learning Latent Groups with Hinge-loss Markov Random Fields},
  author={Stephen H. Bach},
Probabilistic models with latent variables are powerful tools that can help explain related phenomena by mediating dependencies among them. Learning in the presence of latent variables can be difficult though, because of the difficulty of marginalizing them out, or, more commonly, maximizing a lower bound on the marginal likelihood. In this work, we show how to learn hinge-loss Markov random fields (HL-MRFs) that contain latent variables. HL-MRFs are an expressive class of undirected… CONTINUE READING

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