# PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection

arXiv: https://arxiv.org/abs/2103.00128 · HTML: https://mlsanity.com/paper/2103.00128/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [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)
- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [Lazier Than Lazy Greedy](https://mlsanity.com/paper/1409.7938/) (2014)
- [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)
- [Video Summarization with Attention-Based Encoder-Decoder Networks](https://mlsanity.com/paper/1708.09545/) (2017)
- [Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach](https://mlsanity.com/paper/1707.04960/) (2017)
- [Query-Focused Extractive Video Summarization](https://mlsanity.com/paper/1607.05177/) (2016)
- [Query-adaptive Video Summarization via Quality-aware Relevance Estimation](https://mlsanity.com/paper/1705.00581/) (2017)
- [Implicit Diversity in Image Summarization](https://mlsanity.com/paper/1901.10265/) (2019)
- [Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance](https://mlsanity.com/paper/1901.01153/) (2019)
- [A Framework towards Domain Specific Video Summarization](https://mlsanity.com/paper/1809.08854/) (2018)

## Built on by (most cited first, 30 of 51)

- [Submodularity In Machine Learning and Artificial Intelligence](https://mlsanity.com/paper/2202.00132/) (2022)
- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities](https://mlsanity.com/paper/2208.00549/) (2022)
- [A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances](https://mlsanity.com/paper/2505.17799/) (2025)
- [Black-Box Batch Active Learning for Regression](https://mlsanity.com/paper/2302.08981/) (2023)
- [CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification](https://mlsanity.com/paper/2210.01520/) (2022)
- [Active Fine-Tuning of Multi-Task Policies](https://mlsanity.com/paper/2410.05026/) (2024)
- [Submodlib: A Submodular Optimization Library](https://mlsanity.com/paper/2202.10680/) (2022)
- [To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation](https://mlsanity.com/paper/2403.09259/) (2024)
- [SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection](https://mlsanity.com/paper/2407.02665/) (2024)
- [SCoRe: Submodular Combinatorial Representation Learning](https://mlsanity.com/paper/2310.00165/) (2023)
- [Active Data Discovery: Mining Unknown Data using Submodular Information Measures](https://mlsanity.com/paper/2206.08566/) (2022)
- [A Survey of Dataset Refinement for Problems in Computer Vision Datasets](https://mlsanity.com/paper/2210.11717/) (2022)
- [Contributing Dimension Structure of Deep Feature for Coreset Selection](https://mlsanity.com/paper/2401.16193/) (2024)
- [Calibrated Dataset Condensation for Faster Hyperparameter Search](https://mlsanity.com/paper/2405.17535/) (2024)
- [Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection](https://mlsanity.com/paper/2504.20644/) (2025)
- [Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery](https://mlsanity.com/paper/2202.03593/) (2022)
- [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)
- [Sub-SA: Strengthen In-context Learning via Submodular Selective Annotation](https://mlsanity.com/paper/2407.05693/) (2024)
- [Looking Beyond the Known: Towards a Data Discovery Guided Open-World Object Detection](https://mlsanity.com/paper/2510.00303/) (2025)
- [PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information](https://mlsanity.com/paper/2201.12928/) (2022)
- [Spectral Greedy Coresets for Graph Neural Networks](https://mlsanity.com/paper/2405.17404/) (2024)
- [Rooted Absorbed Prefix Trajectory Balance with Submodular Replay for GFlowNet Training](https://mlsanity.com/paper/2603.00454/) (2026)
- [STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings](https://mlsanity.com/paper/2305.10643/) (2023)
- [Bayesian Active Learning for Censored Regression](https://mlsanity.com/paper/2402.11973/) (2024)
- [COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation](https://mlsanity.com/paper/2412.17684/) (2024)
- [LifelongPR: Lifelong point cloud place recognition based on sample replay and prompt learning](https://mlsanity.com/paper/2507.10034/) (2025)
- [DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures](https://mlsanity.com/paper/2210.01526/) (2022)
- [Tell Me What Is Good About This Property: Leveraging Reviews For Segment-Personalized Image Collection Summarization](https://mlsanity.com/paper/2310.19743/) (2023)
- [Theoretical Analysis of Submodular Information Measures for Targeted Data Subset Selection](https://mlsanity.com/paper/2402.13454/) (2024)

## Abstract (arXiv)

With ever-increasing dataset sizes, subset selection techniques are becoming increasingly important for a plethora of tasks. It is often necessary to guide the subset selection to achieve certain desiderata, which includes focusing or targeting certain data points, while avoiding others. Examples of such problems include: i)targeted learning, where the goal is to find subsets with rare classes or rare attributes on which the model is underperforming, and ii)guided summarization, where data (e.g., image collection, text, document or video) is summarized for quicker human consumption with specific additional user intent. Motivated by such applications, we present PRISM, a rich class of PaRameterIzed Submodular information Measures. Through novel functions and their parameterizations, PRISM offers a variety of modeling capabilities that enable a trade-off between desired qualities of a subset like diversity or representation and similarity/dissimilarity with a set of data points. We demonstrate how PRISM can be applied to the two real-world problems mentioned above, which require guided subset selection. In doing so, we show that PRISM interestingly generalizes some past work, therein reinforcing its broad utility. Through extensive experiments on diverse datasets, we demonstrate the superiority of PRISM over the state-of-the-art in targeted learning and in guided image-collection summarization