TAP couples a learner-conditioned policy with diffusion inpainting to generate and selectively inject high-utility tabular augmentations, yielding up to 15.6 pp accuracy gains and 32% RMSE reduction on seven datasets under severe scarcity.
International conference on machine learning , pages=
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NeuFS selects active few-shot samples for LLMs by representing samples via neuron activation patterns and applying a dual-criteria strategy of diversity and neuron consensus to identify informative examples.
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.
citing papers explorer
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Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
TAP couples a learner-conditioned policy with diffusion inpainting to generate and selectively inject high-utility tabular augmentations, yielding up to 15.6 pp accuracy gains and 32% RMSE reduction on seven datasets under severe scarcity.
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Neuron-Aware Active Few-Shot Learning for LLMs
NeuFS selects active few-shot samples for LLMs by representing samples via neuron activation patterns and applying a dual-criteria strategy of diversity and neuron consensus to identify informative examples.
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Are Candidate Models Really Needed for Active Learning?
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.