Geometric Characterisation and Structured Trajectory Surrogates for Clinical Dataset Condensation
Dataset condensation constructs compact synthetic datasets that retain the training utility of large real-world datasets, enabling efficient model development and potentially supporting downstream research in governed domains such as healthcare. Trajectory matching (TM) is a widely used condensation approach that supervises synthetic data using changes in model parameters observed during training on real data, yet the structure of this supervision signal remains poorly understood. In this paper, we provide a geometric characterisation of trajectory matching, showing that a fixed synthetic dataset can only reproduce a limited span of such training-induced parameter changes. When the result...
| Authors | Pafue Christy Nganjimi, Andrew Soltan, Danielle Belgrave et al. |
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| Primary category | cs.LG |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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