10 citations · 24 across the 8 of their papers we have counts for
8 papers · 1 filter
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Avinava Dubey, Michael Minyi Zhang, Eric P. Xing +1
Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow. We exploit the f…
Avoiding Resentment Via Monotonic Fairness
Guy W. Cole, Sinead A. Williamson
Classifiers that achieve demographic balance by explicitly using protected attributes such as race or gender are often politically or culturally controversial due to their lack of…
A New Class of Time Dependent Latent Factor Models with Applications
Sinead A. Williamson, Michael Minyi Zhang, Paul Damien
In many applications, observed data are influenced by some combination of latent causes. For example, suppose sensors are placed inside a building to record responses such as tempe…
Importance Weighted Generative Networks
Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen +3
Deep generative networks can simulate from a complex target distribution, by minimizing a loss with respect to samples from that distribution. However, often we do not have direct…
Parallel Markov Chain Monte Carlo for the Indian Buffet Process
Michael M. Zhang, Avinava Dubey, Sinead A. Williamson
Indian Buffet Process based models are an elegant way for discovering underlying features within a data set, but inference in such models can be slow. Inferring underlying features…
Exact and Efficient Parallel Inference for Nonparametric Mixture Models
Sinead A. Williamson, Avinava Dubey, Eric P. Xing
Nonparametric mixture models based on the Dirichlet process are an elegant alternative to finite models when the number of underlying components is unknown, but inference in such m…