# MYCROFT: Towards Effective and Efficient External Data Augmentation

arXiv: https://arxiv.org/abs/2410.08432 · HTML: https://mlsanity.com/paper/2410.08432/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 56 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding](https://mlsanity.com/paper/1604.01753/) (2016)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Selection via Proxy: Efficient Data Selection for Deep Learning](https://mlsanity.com/paper/1906.11829/) (2019)
- [GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training](https://mlsanity.com/paper/2103.00123/) (2021)
- [GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning](https://mlsanity.com/paper/2012.10630/) (2020)
- [Adversarial Active Learning for Deep Networks: a Margin Based Approach](https://mlsanity.com/paper/1802.09841/) (2018)
- [DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning](https://mlsanity.com/paper/2204.08499/) (2022)
- [Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data](https://mlsanity.com/paper/1912.07768/) (2019)
- [Data Augmentation by Pairing Samples for Images Classification](https://mlsanity.com/paper/1801.02929/) (2018)
- [RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning](https://mlsanity.com/paper/2106.07760/) (2021)
- [Restricted Strong Convexity Implies Weak Submodularity](https://mlsanity.com/paper/1612.00804/) (2016)
- [Learning to Generate Synthetic Data via Compositing](https://mlsanity.com/paper/1904.05475/) (2019)
- [Unicom: Universal and Compact Representation Learning for Image Retrieval](https://mlsanity.com/paper/2304.05884/) (2023)
- [In Defense of Core-set: A Density-aware Core-set Selection for Active Learning](https://mlsanity.com/paper/2206.04838/) (2022)
- [Probabilistic Bilevel Coreset Selection](https://mlsanity.com/paper/2301.09880/) (2023)
- [NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation](https://mlsanity.com/paper/2310.08543/) (2023)
- [LSH Ensemble: Internet-Scale Domain Search](https://mlsanity.com/paper/1603.07410/) (2016)
- [Internet Explorer: Targeted Representation Learning on the Open Web](https://mlsanity.com/paper/2302.14051/) (2023)
- [Auctus: A Dataset Search Engine for Data Augmentation](https://mlsanity.com/paper/2102.05716/) (2021)
- [Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources](https://mlsanity.com/paper/2307.02460/) (2023)
- [Robust Variational Autoencoder for Tabular Data with Beta Divergence](https://mlsanity.com/paper/2006.08204/) (2020)
- [Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks](https://mlsanity.com/paper/2409.12255/) (2024)
- [LEAF: Navigating Concept Drift in Cellular Networks](https://mlsanity.com/paper/2109.03011/) (2021)

## Built on by (most cited first, 1 of 1)

- [Quantifying the Privacy Implications of High-Fidelity Synthetic Network Traffic](https://mlsanity.com/paper/2511.20497/) (2025)

## Abstract (arXiv)

Machine learning (ML) models often require large amounts of data to perform well. When the available data is limited, model trainers may need to acquire more data from external sources. Often, useful data is held by private entities who are hesitant to share their data due to propriety and privacy concerns. This makes it challenging and expensive for model trainers to acquire the data they need to improve model performance. To address this challenge, we propose Mycroft, a data-efficient method that enables model trainers to evaluate the relative utility of different data sources while working with a constrained data-sharing budget. By leveraging feature space distances and gradient matching, Mycroft identifies small but informative data subsets from each owner, allowing model trainers to maximize performance with minimal data exposure. Experimental results across four tasks in two domains show that Mycroft converges rapidly to the performance of the full-information baseline, where all data is shared. Moreover, Mycroft is robust to noise and can effectively rank data owners by utility. Mycroft can pave the way for democratized training of high performance ML models.