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New Provable Techniques for Learning and Inference in Probabilistic Graphical Models

MIT Building E18, Room 304 Ford Building (E18), 50 Ames Street, Cambridge, MA, United States

Speaker: Andrej Risteski (Princeton University) A common theme in machine learning is succinct modeling of distributions over large domains. Probabilistic graphical models are one of the most expressive frameworks for doing this. The two major tasks involving graphical models are learning and inference. Learning is the task of calculating the “best fit” model parameters from…


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