Agreement-Based Learning

The learning of probabilistic models with many hidden variables and non- decomposable dependencies is an important and 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 submodels 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 propose an objective function for our approach, derive EM-style algorithms for parameter estimation, and demonstrate their effectiveness on three challenging real-world learning tasks.

Authors

• Percy Liang Department of Computer Science, Stanford School of Engineering, Stanford, CA, USA Human-Centered Artificial Intelligence Institute, Stanford University, Stanford, CA, USA
• Dan Klein University of California, Berkele
• Michael Jordan

Keywords

No keywords are indexed for this paper.