Amortized Bandwidth Learning for Kernel Density Estimation under Logarithmic Score
Kernel density estimation converts finite samples into probability densities, but its performance depends critically on bandwidth selection. Classical selectors prescribe the sample-to-bandwidth rule analytically or asymptotically, or solve a new optimization for each sample. An amortized framework is proposed that instead learns this mapping across a distribution of density-estimation tasks by optimizing the logarithmic score. A truncated-and-renormalized bounded-support formulation enables stable learning across heterogeneous tasks, while affine standardization allows a selector trained on a single reference interval to transfer across bounded intervals. Experiments under Gaussian sampl...
| Authors | Junyi Liang, Hailiang Du |
|---|---|
| Primary category | cs.LG |
| Published | 20 August 2026 |
| Last updated at source | 19 September 2026 |
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