Feb. 9, 2024, 5:44 a.m. | Nabeel Seedat Nicolas Huynh Boris van Breugel Mihaela van der Schaar

cs.LG updates on arXiv.org arxiv.org

Machine Learning (ML) in low-data settings remains an underappreciated yet crucial problem. Hence, data augmentation methods to increase the sample size of datasets needed for ML are key to unlocking the transformative potential of ML in data-deprived regions and domains. Unfortunately, the limited training set constrains traditional tabular synthetic data generators in their ability to generate a large and diverse augmented dataset needed for ML tasks. To address this challenge, we introduce CLLM, which leverages the prior knowledge of Large …

augmentation cs.ai cs.lg curation data data curation datasets domains key llm llms low machine machine learning sample set synergy tabular training

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