2 citations · 3 across the 5 of their papers we have counts for
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Minimax Optimal Goodness-of-Fit Testing with Kernel Stein Discrepancy
Omar Hagrass, Bharath Sriperumbudur, Krishnakumar Balasubramanian
We explore the minimax optimality of goodness-of-fit tests on general domains using the kernelized Stein discrepancy (KSD). The KSD framework offers a flexible approach for goodnes…
Spectral Regularized Kernel Goodness-of-Fit Tests
Omar Hagrass, Bharath K. Sriperumbudur, Bing Li
Maximum mean discrepancy (MMD) has enjoyed a lot of success in many machine learning and statistical applications, including non-parametric hypothesis testing, because of its abili…
Spectral Regularized Kernel Two-Sample Tests
Omar Hagrass, Bharath K. Sriperumbudur, Bing Li
Over the last decade, an approach that has gained a lot of popularity to tackle nonparametric testing problems on general (i.e., non-Euclidean) domains is based on the notion of re…