5.7k citations · 15.5k across the 75 of their papers we have counts for
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PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick
Generalized linear models (GLMs) -- such as logistic regression, Poisson regression, and robust regression -- provide interpretable models for diverse data types. Probabilistic app…
Parallel MCMC with Generalized Elliptical Slice Sampling
Robert Nishihara, Iain Murray, Ryan P. Adams
Probabilistic models are conceptually powerful tools for finding structure in data, but their practical effectiveness is often limited by our ability to perform inference in them.…
Slice sampling covariance hyperparameters of latent Gaussian models
Iain Murray, Ryan Prescott Adams
The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model. In the Bayesian framework the covariance structure can be spec…
Elliptical slice sampling
Iain Murray, Ryan Prescott Adams, David J. C. MacKay
Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Mon…
Nonparametric Bayesian Density Modeling with Gaussian Processes
Ryan Prescott Adams, Iain Murray, David J. C. MacKay
We present the Gaussian process density sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consi…