arXiv:2606.16045v1 Announce Type: new Abstract: In the data selection problem, the objective is to choose a small, representative subset of data that can be used to efficiently train a machine learning model. Sener and Savarese [ICLR 2018] showed that, given an embedding representation of the data and suitable geometric assumptions, heuristics based on $k$-center clustering can be used to perform data selection. This perspective was further explored by Axiotis et. al. [ICML 2024], who proposed a data selection approach based on $k$-means clustering and sensitivity sampling. However, these meth
The continuous growth of data volumes necessitates more efficient methods for training machine learning models, driving innovation in data selection techniques like active learning.
Improved data selection can significantly reduce the computational resources and time required for AI model training, impacting the efficiency and cost of AI development across industries.
New active learning methodologies, particularly those leveraging low-rank structures, offer more robust and efficient ways to identify crucial data subsets for machine learning.
- · AI developers
- · Cloud providers (for optimized resource use)
- · Companies with large datasets
- · Inefficient AI training methodologies
Reduced computational costs and faster iteration cycles for AI model development.
Democratization of advanced AI model building as resource requirements become less prohibitive.
Acceleration of AI adoption in industries where data efficiency is a critical bottleneck.
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