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3 results for “augmentation”
Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM
A new unsupervised data augmentation method combining Gaussian Mixture Models and Large Language Models is proposed to improve clustering of underrepresented topics in imbalanced NLP datasets.
Aug 3, 2026
Safe Inference-Time Alignment via Lagrangian Reward Augmentation
A new research paper proposes Lagrangian Reward Augmentation (LARA), a framework to integrate explicit safety constraints into inference-time alignment of frozen language models by dualizing constrained optimization and calibrating a single dual variable on a small dataset.
Jul 8, 2026
A Filtered Mixture-of-Generators for Fully Synthetic Survival Training
FoGS is a new synthetic data method for survival analysis that improves model performance on scarce clinical data by filtering outputs from multiple generative models using real-data-trained survival scorers, enabling viable real-data substitution in privacy-restricted settings.
Published Jul 2, 2026 · Analyzed Jul 5, 2026