label-scarcity

This paper evaluates self-supervised pretraining for tabular models under label scarcity and missing data, finding mixed efficacy but reliable improvements under test-time missingness.

This paper studies federated active learning in low-budget regimes, revealing that homogeneous data requires stronger coordination due to heterogeneity reversal. It proposes a framework using federated representation learning to enable globally coordinated active selection, outperforming existing methods.

Gnosys Labs introduces an autonomous model engineering method that improves classifiers under label scarcity, outperforming standard optimizers like GEPA on the ToxicChat benchmark.