Computer Science and Applied Mathematics

Regularized Bayesian Inference
Jun Zhu, Tsinghua University

Existing Bayesian models, especially nonparametric Bayesian methods, rely heavily on specially conceived priors to incorporate domain knowledge for discovering improved latent representations. While priors can affect posterior distributions through Bayes' theorem, imposing posterior regularization is arguably more direct and in some cases can be more natural and easier. In this talk, I present regularized Bayesian inference, a computational framework to perform posterior inference with a convex regularization on the desired post-data posterior distributions. Furthermore, I present two concrete examples for classification and social link prediction.

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Computer Science and Applied Mathematics

Kavli Frontiers of Science PRO

The Kavli Frontiers of Science symposium series is the National Academy of Science’s premiere activity for distinguished young scientists. Unlike meetings that focus on a narrow area of science, these meetings allow participants to explore innovative


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The Kavli Frontiers of Science symposium series is the National Academy of Science’s premiere activity for distinguished young scientists. Unlike meetings that focus on a narrow area of science, these meetings allow participants to explore innovative research ideas across a wide variety of fields and to develop new networks that will serve them as they progress in their careers..

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