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• W3172219754 abstract "We study the complexity of Stein Variational Gradient Descent (SVGD), which is an algorithm to sample from $pi(x) propto exp(-F(x))$ where $F$ smooth and nonconvex. We provide a clean complexity bound for SVGD in the population limit in terms of the Stein Fisher Information (or squared Kernelized Stein Discrepancy), as a function of the dimension of the problem $d$ and the desired accuracy $varepsilon$. Unlike existing work, we do not make any assumption on the trajectory of the algorithm. Instead, our key assumption is that the target distribution satisfies Talagrand's inequality T1." @default.
• W3172219754 created "2021-06-22" @default.
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• W3172219754 date "2021-06-06" @default.
• W3172219754 modified "2023-09-27" @default.
• W3172219754 title "Complexity Analysis of Stein Variational Gradient Descent Under Talagrand's Inequality T1" @default.
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