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Tight Sampling Complexity with stochastic gradient oracles in Fixed Dimensions
We investigate the stochastic-gradient query complexity of sampling smooth strongly log-concave distributions in any fixed Euclidean dimension. The potential is $μ$-strongly convex and $L$-smooth, with an unknown mode in the ball of radius $μ^{-1/2}$ about the origin. We have access to unbiased stoc
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