6 papers · 1 filter
Controlling Moments with Kernel Stein Discrepancies
Heishiro Kanagawa, Alessandro Barp, Arthur Gretton +1
Kernel Stein discrepancies (KSDs) measure the quality of a distributional approximation and can be computed even when the target density has an intractable normalizing constant. No…
Compress Then Test: Powerful Kernel Testing in Near-linear Time
Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey
Kernel two-sample testing provides a powerful framework for distinguishing any pair of distributions based on sample points. However, existing kernel tests either run in …
Targeted Separation and Convergence with Kernel Discrepancies
Alessandro Barp, Carl-Johann Simon-Gabriel, Mark Girolami +1
Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD) have grown central to a wide range of applications, including hypothesis testing, sampler selection, distr…
Generalized Kernel Thinning
Raaz Dwivedi, Lester Mackey
The kernel thinning (KT) algorithm of Dwivedi and Mackey (2021) compresses a probability distribution more effectively than independent sampling by targeting a reproducing kernel H…
Kernel Thinning
Raaz Dwivedi, Lester Mackey
We introduce kernel thinning, a new procedure for compressing a distribution more effectively than i.i.d. sampling or standard thinning. Given a suitable reproducing k…
Gradient Estimation with Discrete Stein Operators
Jiaxin Shi, Yuhao Zhou, Jessica Hwang +2
Gradient estimation -- approximating the gradient of an expectation with respect to the parameters of a distribution -- is central to the solution of many machine learning problems…