15.1k citations · 36.9k across the 152 of their papers we have counts for
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The Variational Fair Autoencoder
Christos Louizos, Kevin Swersky, Yujia Li +2
We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining…
Scalable MCMC for Mixed Membership Stochastic Blockmodels
Wenzhe Li, Sungjin Ahn, Max Welling
We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in mixed-membership stochastic blockmodels (MMSB). Our algorithm is based on th…
Variational Dropout and the Local Reparameterization Trick
Diederik P. Kingma, Tim Salimans, Max Welling
We investigate a local reparameterizaton technique for greatly reducing the variance of stochastic gradients for variational Bayesian inference (SGVB) of a posterior over model par…
Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
Sungjin Ahn, Anoop Korattikara, Nathan Liu +2
Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid over-fitting, Bayesian Matrix Factorization has not b…