5.7k citations · 15.5k across the 75 of their papers we have counts for
4 papers · 1 filter
Bayesian inference for Matérn repulsive processes
Vinayak Rao, Ryan P. Adams, David B. Dunson
In many applications involving point pattern data, the Poisson process assumption is unrealistic, with the data exhibiting a more regular spread. Such a repulsion between events is…
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures
Dan Lovell, Jonathan Malmaud, Ryan P. Adams +1
The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian nonparametric modeling, and is widely used in tasks such as density estimation, natural language processi…
High-Dimensional Probability Estimation with Deep Density Models
Oren Rippel, Ryan Prescott Adams
One of the fundamental problems in machine learning is the estimation of a probability distribution from data. Many techniques have been proposed to study the structure of data, mo…
Gaussian Process Kernels for Pattern Discovery and Extrapolation
Andrew Gordon Wilson, Ryan Prescott Adams
Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation. We introduce simple closed form kernels th…