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Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
Yutian Chen, Max Welling
In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields. These methods are mostly based on L1…
Hybrid Variational/Gibbs Collapsed Inference in Topic Models
Max Welling, Yee Whye Teh, Hilbert Kappen
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvanta…
On Smoothing and Inference for Topic Models
Arthur Asuncion, Max Welling, Padhraic Smyth +1
Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data. Various learning algorithms have been develop…
Herding Dynamic Weights for Partially Observed Random Field Models
Max Welling
Learning the parameters of a (potentially partially observable) random field model is intractable in general. Instead of focussing on a single optimal parameter value we propose to…
Super-Samples from Kernel Herding
Yutian Chen, Max Welling, Alex Smola
We extend the herding algorithm to continuous spaces by using the kernel trick. The resulting "kernel herding" algorithm is an infinite memory deterministic process that learns to…