# Coresets for Data-efficient Training of Machine Learning Models

arXiv: https://arxiv.org/abs/1906.01827 · HTML: https://mlsanity.com/paper/1906.01827/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 9 of 9)

- [Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning](https://mlsanity.com/paper/2505.20161/) (2025)
- [Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions](https://mlsanity.com/paper/2401.04305/) (2024)
- [Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning](https://mlsanity.com/paper/2411.15796/) (2024)
- [Deep reinforced active learning for multi-class image classification](https://mlsanity.com/paper/2206.13391/) (2022)
- [Memory Classifiers: Two-stage Classification for Robustness in Machine Learning](https://mlsanity.com/paper/2206.05323/) (2022)
- [Coresets Before Score Sets: Evaluation-Unsupervised Prompt Subset Selection for LLM Benchmarks](https://mlsanity.com/paper/2607.09739/) (2026)
- [Prioritizing Informative Features and Examples for Deep Learning from Noisy Data](https://mlsanity.com/paper/2403.00013/) (2024)
- [Less is More: Selective Reduction of CT Data for Self-Supervised Pre-Training of Deep Learning Models with Contrastive Learning Improves Downstream Classification Performance](https://mlsanity.com/paper/2410.14524/) (2024)
- [MODE: Multi-Objective Adaptive Coreset Selection](https://mlsanity.com/paper/2512.21152/) (2025)

## Abstract (arXiv)

Incremental gradient (IG) methods, such as stochastic gradient descent and its variants are commonly used for large scale optimization in machine learning. Despite the sustained effort to make IG methods more data-efficient, it remains an open question how to select a training data subset that can theoretically and practically perform on par with the full dataset. Here we develop CRAIG, a method to select a weighted subset (or coreset) of training data that closely estimates the full gradient by maximizing a submodular function. We prove that applying IG to this subset is guaranteed to converge to the (near)optimal solution with the same convergence rate as that of IG for convex optimization. As a result, CRAIG achieves a speedup that is inversely proportional to the size of the subset. To our knowledge, this is the first rigorous method for data-efficient training of general machine learning models. Our extensive set of experiments show that CRAIG, while achieving practically the same solution, speeds up various IG methods by up to 6x for logistic regression and 3x for training deep neural networks.