Web: http://arxiv.org/abs/2202.00824

June 23, 2022, 1:12 a.m. | Antonin Schrab, Benjamin Guedj, Arthur Gretton

stat.ML updates on arXiv.org arxiv.org

We investigate properties of goodness-of-fit tests based on the Kernel Stein
Discrepancy (KSD). We introduce a strategy to construct a test, called KSDAgg,
which aggregates multiple tests with different kernels. KSDAgg avoids splitting
the data to perform kernel selection (which leads to a loss in test power), and
rather maximises the test power over a collection of kernels. We provide
theoretical guarantees on the power of KSDAgg: we show it achieves the smallest
uniform separation rate of the collection, up …

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