# Theoretical Analysis of Submodular Information Measures for Targeted Data Subset Selection

arXiv: https://arxiv.org/abs/2402.13454 · HTML: https://mlsanity.com/paper/2402.13454/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 17 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Submodular Combinatorial Information Measures with Applications in Machine Learning](https://mlsanity.com/paper/2006.15412/) (2020)
- [SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios](https://mlsanity.com/paper/2107.00717/) (2021)
- [PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection](https://mlsanity.com/paper/2103.00128/) (2021)
- [Submodularity In Machine Learning and Artificial Intelligence](https://mlsanity.com/paper/2202.00132/) (2022)
- [TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information](https://mlsanity.com/paper/2112.00166/) (2021)
- [CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification](https://mlsanity.com/paper/2210.01520/) (2022)
- [A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems](https://mlsanity.com/paper/1902.10176/) (2019)
- [Active Data Discovery: Mining Unknown Data using Submodular Information Measures](https://mlsanity.com/paper/2206.08566/) (2022)
- [DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation](https://mlsanity.com/paper/2110.04908/) (2021)
- [Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification](https://mlsanity.com/paper/2306.01277/) (2023)
- [PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information](https://mlsanity.com/paper/2201.12928/) (2022)
- [STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings](https://mlsanity.com/paper/2305.10643/) (2023)
- [DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures](https://mlsanity.com/paper/2210.01526/) (2022)

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

- [SMA: Submodular Modality Aligner For Data Efficient Multimodal Learning](https://mlsanity.com/paper/2605.12872/) (2026)

## Abstract (arXiv)

With increasing volume of data being used across machine learning tasks, the capability to target specific subsets of data becomes more important. To aid in this capability, the recently proposed Submodular Mutual Information (SMI) has been effectively applied across numerous tasks in literature to perform targeted subset selection with the aid of a exemplar query set. However, all such works are deficient in providing theoretical guarantees for SMI in terms of its sensitivity to a subset's relevance and coverage of the targeted data. For the first time, we provide such guarantees by deriving similarity-based bounds on quantities related to relevance and coverage of the targeted data. With these bounds, we show that the SMI functions, which have empirically shown success in multiple applications, are theoretically sound in achieving good query relevance and query coverage.