Stationary MMD points show super-convergence in integration error over MMD for RKHS integrands, and MMD gradient flows compute them with a new non-asymptotic finite-particle error bound.
Efficient numerical integration in reproducing kernel Hilbert spaces via leverage scores sampling
2 Pith papers cite this work. Polarity classification is still indexing.
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Derives MSIP algorithm from MMD gradient flows for weighted quantization, extending mean shift and relating to preconditioned gradient descent and Lloyd's clustering.
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Stationary MMD Points
Stationary MMD points show super-convergence in integration error over MMD for RKHS integrands, and MMD gradient flows compute them with a new non-asymptotic finite-particle error bound.
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Weighted quantization using MMD: From mean field to mean shift via gradient flows
Derives MSIP algorithm from MMD gradient flows for weighted quantization, extending mean shift and relating to preconditioned gradient descent and Lloyd's clustering.