4 papers
Optimal quantisation of probability measures using maximum mean discrepancy
Onur Teymur, Jackson Gorham, Marina Riabiz +1
Several researchers have proposed minimisation of maximum mean discrepancy (MMD) as a method to quantise probability measures, i.e., to approximate a target distribution by a repre…
Stochastic Stein Discrepancies
Jackson Gorham, Anant Raj, Lester Mackey
Stein discrepancies (SDs) monitor convergence and non-convergence in approximate inference when exact integration and sampling are intractable. However, the computation of a Stein…
Stein Point Markov Chain Monte Carlo
Wilson Ye Chen, Alessandro Barp, François-Xavier Briol +4
An important task in machine learning and statistics is the approximation of a probability measure by an empirical measure supported on a discrete point set. Stein Points are a cla…
Stein Points
Wilson Ye Chen, Lester Mackey, Jackson Gorham +2
An important task in computational statistics and machine learning is to approximate a posterior distribution with an empirical measure supported on a set of representative…