Dataset Similarity

Dataset similarity research focuses on developing robust methods to quantify the resemblance between datasets, crucial for evaluating model generalization, detecting data drift, and optimizing federated learning. Current efforts concentrate on creating dataset-agnostic metrics that are computationally efficient and privacy-preserving, often leveraging techniques like prototype-based representations or feature-importance analysis, and moving beyond simple distance measures to incorporate downstream task performance. These advancements are vital for improving the reliability of machine learning model evaluations and enhancing the efficiency and trustworthiness of data-driven applications across various domains.

Papers