Dataset Pruning from First Principles: A Label-Free Linear Programming Approach
Dataset pruning reduces a large training set to a representative subset while preserving model performance. Existing geometry-based methods typically assume that nearby points in embedding space share similar properties. Rather than imposing this assumption, we derive geometric selection criteria by reformulating unbiased subset selection as a variance minimization problem. Unbiasedness ensures that unweighted subset averages recover full-dataset averages in expectation, including losses and gradients at fixed model parameters. Specifically, we characterize a family of unbiased subset selection algorithms as a high-dimensional polytope. In this context, minimizing the expected sampling va...
| Authors | Rodrigo Schuller, Francisco Ganacim |
|---|---|
| Primary category | stat.ML |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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