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The paper proposes UASPL, a method that integrates predictive reliability into sample selection for self-paced learning using evidential neural networks, improving classification performance and interpretability.
Introduces K-ABENA, a selective gradient computation framework that uses compensated loss-based sample exclusion with unbiased gradient estimation, proving convergence guarantees and showing compute savings of 28-54% without performance degradation across various datasets.
RADS uses reinforcement learning to pick the most informative samples for few-shot fine-tuning, boosting transfer-learning accuracy on low-resource, highly imbalanced clinical datasets.
This paper proposes ACSESS, a method for automatically combining multiple sample selection strategies to improve few-shot learning across both in-context learning and gradient-based approaches. The work demonstrates that combining strategies consistently outperforms individual selection methods across 14 datasets with both text and image modalities.
DataFlex is a unified framework for data-centric dynamic training of LLMs that integrates sample selection, domain mixture adjustment, and sample reweighting with compatibility for standard workflows and efficient large-scale deployment, achieving consistent improvements over static training.