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stat.ML2014★ 3 cited
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs
Yutian Chen, Vikash Mansinghka, Zoubin Ghahramani
Probabilistic programming languages can simplify the development of machine learning techniques, but only if inference is sufficiently scalable. Unfortunately, Bayesian parameter e…
stat.ML2012★ 6 cited
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…