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Agreement-Based Learning
Percy Liang · Dan Klein · Michael Jordan
The learning of probabilistic models with many hidden variables and non-decomposable dependencies is an important but challenging problem. In contrast to traditional approaches based on approximate inference in a single intractable model, our approach is to train a set of tractable component models by encouraging them to agree on the hidden variables. This allows us to capture non-decomposable aspects of the data while still maintaining tractability. We exhibit an objective function for our approach, derive EM-style algorithms for parameter estimation, and demonstrate their effectiveness on three challenging real-world learning tasks.
Author Information
Percy Liang (Stanford University)
Dan Klein (UC Berkeley)
Michael Jordan (UC Berkeley)
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2007 Poster: Agreement-Based Learning »
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2008 Spotlight: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
2008 Spotlight: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2008 Poster: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice »
Zhihua Zhang · Michael Jordan · Dit-Yan Yeung -
2008 Poster: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification »
Simon Lacoste-Julien · Fei Sha · Michael Jordan -
2008 Spotlight: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice »
Zhihua Zhang · Michael Jordan · Dit-Yan Yeung -
2008 Poster: Efficient Inference in Phylogenetic InDel Trees »
Alexandre Bouchard-Côté · Michael Jordan · Dan Klein -
2008 Poster: Spectral Clustering with Perturbed Data »
Ling Huang · Donghui Yan · Michael Jordan · Nina Taft -
2008 Spotlight: Efficient Inference in Phylogenetic InDel Trees »
Alexandre Bouchard-Côté · Michael Jordan · Dan Klein -
2008 Spotlight: Spectral Clustering with Perturbed Data »
Ling Huang · Donghui Yan · Michael Jordan · Nina Taft -
2007 Session: Spotlights »
Dan Klein -
2007 Session: Spotlights »
Dan Klein -
2007 Spotlight: Discriminative Log-Linear Grammars with Latent Variables »
Slav Petrov · Dan Klein -
2007 Spotlight: Resampling Methods for Protein Structure Prediction with Rosetta »
Ben Blum · David Baker · Michael Jordan · Philip Bradley · Rhiju Das · David Kim -
2007 Spotlight: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
XuanLong Nguyen · Martin J Wainwright · Michael Jordan -
2007 Poster: Discriminative Log-Linear Grammars with Latent Variables »
Slav Petrov · Dan Klein -
2007 Poster: Resampling Methods for Protein Structure Prediction with Rosetta »
Ben Blum · David Baker · Michael Jordan · Philip Bradley · Rhiju Das · David Kim -
2007 Poster: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
XuanLong Nguyen · Martin J Wainwright · Michael Jordan -
2007 Poster: A Probabilistic Approach to Language Change »
Alexandre Bouchard-Côté · Percy Liang · Tom Griffiths · Dan Klein -
2006 Poster: Distributed PCA and Network Anomaly Detection »
Ling Huang · XuanLong Nguyen · Minos Garofalakis · Michael Jordan · Anthony D Joseph · Nina Taft