# Submodular Mutual Information for Targeted Data Subset Selection

arXiv: https://arxiv.org/abs/2105.00043 · HTML: https://mlsanity.com/paper/2105.00043/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 30 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning](https://mlsanity.com/paper/2012.10630/) (2020)
- [Submodular Combinatorial Information Measures with Applications in Machine Learning](https://mlsanity.com/paper/2006.15412/) (2020)
- [Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision](https://mlsanity.com/paper/1901.01151/) (2019)
- [Query-adaptive Video Summarization via Quality-aware Relevance Estimation](https://mlsanity.com/paper/1705.00581/) (2017)
- [A Unified Framework for Generic, Query-Focused, Privacy Preserving and Update Summarization using Submodular Information Measures](https://mlsanity.com/paper/2010.05631/) (2020)

## Built on by (most cited first, 8 of 8)

- [A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances](https://mlsanity.com/paper/2505.17799/) (2025)
- [MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning](https://mlsanity.com/paper/2301.13287/) (2023)
- [RD-DPP: Rate-Distortion Theory Meets Determinantal Point Process to Diversify Learning Data Samples](https://mlsanity.com/paper/2304.04137/) (2023)
- [Learning on Bandwidth Constrained Multi-Source Data with MIMO-inspired DPP MAP Inference](https://mlsanity.com/paper/2306.02497/) (2023)
- [UniPROT: Uniform Prototype Selection via Partial Optimal Transport with Submodular Guarantees](https://mlsanity.com/paper/2604.10952/) (2026)
- [SCORE: Soft Label Compression-Centric Dataset Condensation via Coding Rate Optimization](https://mlsanity.com/paper/2503.13935/) (2025)
- [Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information](https://mlsanity.com/paper/2508.07713/) (2025)
- [SubZeroCore: A Submodular Approach with Zero Training for Coreset Selection](https://mlsanity.com/paper/2509.21748/) (2025)

## Abstract (arXiv)

With the rapid growth of data, it is becoming increasingly difficult to train or improve deep learning models with the right subset of data. We show that this problem can be effectively solved at an additional labeling cost by targeted data subset selection(TSS) where a subset of unlabeled data points similar to an auxiliary set are added to the training data. We do so by using a rich class of Submodular Mutual Information (SMI) functions and demonstrate its effectiveness for image classification on CIFAR-10 and MNIST datasets. Lastly, we compare the performance of SMI functions for TSS with other state-of-the-art methods for closely related problems like active learning. Using SMI functions, we observe ~20-30% gain over the model's performance before re-training with added targeted subset; ~12% more than other methods.