← 研踪 PaperTrailarXiv:2611.01828 · v1
Instance-Specific Sharp Convergence Rate of the Add-$λ$ Estimator via Feature Space Decomposition
首次提交 2026-11-01 19:57
版本更新 2026-11-01 19:57
雷达观测 2026-11-04 23:43
摘要
Using the Feature Space Decomposition framework, we establish non-asymptotic matching upper and lower bounds for the expected Kullback-Leibler estimation error of the Add-$λ$ estimator of a discrete probability distribution, for any signal, any dimension, any sample size, and any $λ>0$.
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v1 · 2026-11-01 19:57 · 最新观测版本
Instance-Specific Sharp Convergence Rate of the Add-$λ$ Estimator via Feature Space Decomposition
Using the Feature Space Decomposition framework, we establish non-asymptotic matching upper and lower bounds for the expected Kullback-Leibler estimation error of the Add-$λ$ estimator of a discrete probability distribution, for any signal, any dimension, any sample size, and any $λ>0$.
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