Statistics and Data Science Seminar Series
New Provable Techniques for Learning and Inference in Probabilistic Graphical Models
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…
