Graphical Models: Modeling, Optimization, and Hilbert Space Embedding
Over the past two decades graphical models have been widely used as a powerful tool for compactly representing distributions. On the other hand, kernel methods have also been used extensively to come up with rich representations. This thesis aims to combine graphical models with kernels to produce compact models with rich representational abilities. The following four areas are our focus. 1. Conditional random fields for multi-agent reinforcement learning. Conditional random fields (CRFs)...[Show more]
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