Agreement-Based Learning

The learning of probabilistic models with many hidden variables and nondecomposable 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.

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Percy Liang, Dan Klein, Michael I. Jordan
• Keywords: Challenging Problem, Hidden Variables, Information Technology, NIPS 2007, Single Intractable Model