AI & ML
Minimax Rates for Estimating Powered Sliced Wasserstein Functionals
arXiv stat.ML0 views
arXiv:2610.12740v1 Announce Type: cross Abstract: We study the two-sample estimation of the powered Sliced Wasserstein functional under projected $r$-th moment assumptions. Let $N=n\wedge m$, where $n$ and $m$ are two sample sizes. We establish the minimax rates in the noncritical regimes. When $p2p$ it is of order $N^{-1/2}$. In both cases, the natural plug-in estimator is minimax rate-optimal. At the endpoint $r=p$, the minimax risk is bounded away from zero, so uniformly consistent estimation is impossible. At the critical order $r=2p$, we obtain the exact worst-case rate of the plug-in estimator and a lower bound for the minimax risk, leaving a logarithmic gap between the lower and upper bounds. The exact minimax rate in this critical case remains open.
Source:https://arxiv.org/abs/2610.12740PreviousKernelized Parameter Sensitivity Atlases for Amortized Conditional InterpretabilityNextMinimum Bounding Sets for N-1-1 Branch Contingencies Under Thermal and Voltage Limits in Linear Network Models
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