t-divergence Based Approximate Inference

Part of Advances in Neural Information Processing Systems 24 (NIPS 2011)

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Nan Ding, Yuan Qi, S.v.n. Vishwanathan


Approximate inference is an important technique for dealing with large, intractable graphical models based on the exponential family of distributions. We extend the idea of approximate inference to the t-exponential family by defining a new t-divergence. This divergence measure is obtained via convex duality between the log-partition function of the t-exponential family and a new t-entropy. We illustrate our approach on the Bayes Point Machine with a Student's t-prior.