Subsampling for supervised learning in reproducing kernel Hilbert spaces
In the era of big data, subsampling became a common practice in statistical learning. By selecting a subgroup of individuals based on which the learner is trained, subsampling aims at reducing the computational cost and time of the estimation step, and ideally leads to a decrease of its energy consumption and carbon footprint. This work focuses on a nonparametric setting, in which the hypotheses set lies in a reproducing kernel Hilbert space, and the estimator is a minimizer of an empirical risk reweighted à la Horvitz-Thompson. By studying the asymptotic properties of this estimator, we reveal an optimal subsampling scheme (regarding the trace of the covariance operator) and show that it...
| Authors | Eyal Vayness, Maxime Sangnier |
|---|---|
| Primary category | stat.ML |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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