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Discriminative latent-variable models are typically learned using EM or gradient-based optimization, which suffer from local optima.
Adaptive mixtures of local experts
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E · 1991
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Modeling with mixtures of linear regressions
Viele, Kert and Tong, Barbara · 2002
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Conditional random fields for object recognition
Quattoni, A., Collins, M., and Darrell, T · 2004
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An end-to-end discriminative approach to machine translation
Liang, P., Bouchard-Côté, A., Klein, D., and Taskar, B · 2006
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Discriminative log-linear grammars with latent variables
Petrov, S. and Klein, D · 2008
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A spectral algorithm for learning hidden Markov models
Hsu, D., Kakade, S. M., and Zhang, T · 2009
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Estimation of (near) low-rank matrices with noise and high-dimensional scaling
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Hilbert space embeddings of hidden Markov models
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Spectral experts for estimating mixtures of linear regressions
Chaganty, A. and Liang, P · 2013
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Learning mixtures of spherical gaussians: Moment methods and spectral decompositions
Hsu, D. and Kakade, S. M · 2013
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Spectral learning of latent-variable PCFGs
Cohen, S. B., Stratos, K., Collins, M., Foster, D. P., and Ungar, L · 2012
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Two SVDs suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation
Anandkumar, A., Foster, D. P., Hsu, D., Kakade, S. M., and Liu, Y
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A method of moments for mixture models and hidden Markov models
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Tensor decompositions for learning latent variable models
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