15.1k citations · 36.9k across the 152 of their papers we have counts for
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Auto-Encoding Variational Bayes
Diederik P Kingma, Max Welling
How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and l…
Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation
James Foulds, Levi Boyles, Christopher Dubois +2
In the internet era there has been an explosion in the amount of digital text information available, leading to difficulties of scale for traditional inference algorithms for topic…
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
Anoop Korattikara, Yutian Chen, Max Welling
Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets? We argue that computing the likelihood for N datapoints in the Metropolis-Hastings…
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation
Max Welling, Yee Whye Teh
We present a novel inference algorithm for arbitrary, binary, undirected graphs. Unlike loopy belief propagation, which iterates fixed point equations, we directly descend on the B…
Herded Gibbs Sampling
Luke Bornn, Yutian Chen, Nando de Freitas +3
The Gibbs sampler is one of the most popular algorithms for inference in statistical models. In this paper, we introduce a herding variant of this algorithm, called herded Gibbs, t…