A Coreset Learning Reality Check

• URL: http://arxiv.org/abs/2301.06163v1
• Date: Sun, 15 Jan 2023 19:26:17 GMT
• Title: A Coreset Learning Reality Check
• Authors: Fred Lu, Edward Raff, James Holt
• Abstract summary: Subsampling algorithms are a natural approach to reduce data size before fitting models on massive datasets. In recent years, several works have proposed methods for subsampling rows from a data matrix while maintaining relevant information for classification. We compare multiple methods for logistic regression drawn from the coreset and optimal subsampling literature and discover inconsistencies in their effectiveness.
• Score: 33.002265576337486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Subsampling algorithms are a natural approach to reduce data size before fitting models on massive datasets. In recent years, several works have proposed methods for subsampling rows from a data matrix while maintaining relevant information for classification. While these works are supported by theory and limited experiments, to date there has not been a comprehensive evaluation of these methods. In our work, we directly compare multiple methods for logistic regression drawn from the coreset and optimal subsampling literature and discover inconsistencies in their effectiveness. In many cases, methods do not outperform simple uniform subsampling.

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