M³ partitions space by physical variation using multi-scale Morton ordering to balance training measures, yielding up to 4.7× lower error on industrial volumetric datasets and outperforming higher-resolution training even after aggressive subsampling.
Coresets for data-efficient training of machine learning models
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Extends influence functions with a second-order pairwise interaction term that improves group attribution accuracy over simple summation on multiple model-dataset pairs and instruction-tuning selection tasks.
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M$^3$: Reframing Training Measures for Discretized Physical Simulations
M³ partitions space by physical variation using multi-scale Morton ordering to balance training measures, yielding up to 4.7× lower error on industrial volumetric datasets and outperforming higher-resolution training even after aggressive subsampling.
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Interaction-Aware Influence Functions for Group Attribution
Extends influence functions with a second-order pairwise interaction term that improves group attribution accuracy over simple summation on multiple model-dataset pairs and instruction-tuning selection tasks.