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Monday, December 7 • 19:00 - 23:59
Probabilistic Variational Bounds for Graphical Models

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Variational algorithms such as tree-reweighted belief propagation can provide deterministic bounds on the partition function, but are often loose and difficult to use in an ``any-time'' fashion, expending more computation for tighter bounds. On the other hand, Monte Carlo estimators such as importance sampling have excellent any-time behavior, but depend critically on the proposal distribution. We propose a simple Monte Carlo based inference method that augments convex variational bounds by adding importance sampling (IS). We argue that convex variational methods naturally provide good IS proposals that ``cover" the probability of the target distribution, and reinterpret the variational optimization as designing a proposal to minimizes an upper bound on the variance of our IS estimator. This both provides an accurate estimator and enables the construction of any-time probabilistic bounds that improve quickly and directly on state of-the-art variational bounds, which provide certificates of accuracy given enough samples relative to the error in the initial bound.


Monday December 7, 2015 19:00 - 23:59
210 C #63

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