111 citations · 271 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
Efficient Parametric Projection Pursuit Density Estimation
Max Welling, Richard S. Zemel, Geoffrey E. Hinton
Product models of low dimensional experts are a powerful way to avoid the curse of dimensionality. We present the ``under-complete product of experts' (UPoE), where each expert mod…
Semisupervised Classifier Evaluation and Recalibration
Peter Welinder, Max Welling, Pietro Perona
How many labeled examples are needed to estimate a classifier's performance on a new dataset? We study the case where data is plentiful, but labels are expensive. We show that by m…
Bayesian Random Fields: The Bethe-Laplace Approximation
Max Welling, Sridevi Parise
While learning the maximum likelihood value of parameters of an undirected graphical model is hard, modelling the posterior distribution over parameters given data is harder. Yet,…
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
Sungjin Ahn, Anoop Korattikara, Max Welling
In this paper we address the following question: Can we approximately sample from a Bayesian posterior distribution if we are only allowed to touch a small mini-batch of data-items…