# Submodlib: A Submodular Optimization Library

arXiv: https://arxiv.org/abs/2202.10680 · HTML: https://mlsanity.com/paper/2202.10680/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 71 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 24 of 24)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Do Deep Nets Really Need to be Deep?](https://mlsanity.com/paper/1312.6184/) (2013)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [Zero-Shot Learning Through Cross-Modal Transfer](https://mlsanity.com/paper/1301.3666/) (2013)
- [Training Very Deep Networks](https://mlsanity.com/paper/1507.06228/) (2015)
- [Video Summarization with Long Short-term Memory](https://mlsanity.com/paper/1605.08110/) (2016)
- [Synthesized Classifiers for Zero-Shot Learning](https://mlsanity.com/paper/1603.00550/) (2016)
- [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)
- [PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection](https://mlsanity.com/paper/2103.00128/) (2021)
- [Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints](https://mlsanity.com/paper/1311.2106/) (2013)
- [Query-adaptive Video Summarization via Quality-aware Relevance Estimation](https://mlsanity.com/paper/1705.00581/) (2017)
- [Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization](https://mlsanity.com/paper/1906.00072/) (2019)
- [apricot: Submodular selection for data summarization in Python](https://mlsanity.com/paper/1906.03543/) (2019)
- [Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance](https://mlsanity.com/paper/1901.01153/) (2019)
- [Polyhedral aspects of Submodularity, Convexity and Concavity](https://mlsanity.com/paper/1506.07329/) (2015)
- [A Framework towards Domain Specific Video Summarization](https://mlsanity.com/paper/1809.08854/) (2018)
- [Concave Aspects of Submodular Functions](https://mlsanity.com/paper/2006.16784/) (2020)

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

- [Diverse Demonstrations Improve In-context Compositional Generalization](https://mlsanity.com/paper/2212.06800/) (2022)
- [Speeding up NAS with Adaptive Subset Selection](https://mlsanity.com/paper/2211.01454/) (2022)
- [STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings](https://mlsanity.com/paper/2305.10643/) (2023)
- [One Shot GANs for Long Tail Problem in Skin Lesion Dataset using novel content space assessment metric](https://mlsanity.com/paper/2409.19945/) (2024)
- [Bayesian Coreset Optimization for Personalized Federated Learning](https://mlsanity.com/paper/2511.01800/) (2025)
- [Active Learning Using Aggregated Acquisition Functions: Accuracy and Sustainability Analysis](https://mlsanity.com/paper/2602.07440/) (2026)
- [STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active Learning](https://mlsanity.com/paper/2402.13468/) (2024)
- [Toward Trustworthy AI: Multi-Target Adversarial Attacks and Robust Defenses for Continuous Data Summarization](https://mlsanity.com/paper/2606.11804/) (2026)

## Abstract (arXiv)

Submodular functions are a special class of set functions which naturally model the notion of representativeness, diversity, coverage etc. and have been shown to be computationally very efficient. A lot of past work has applied submodular optimization to find optimal subsets in various contexts. Some examples include data summarization for efficient human consumption, finding effective smaller subsets of training data to reduce the model development time (training, hyper parameter tuning), finding effective subsets of unlabeled data to reduce the labeling costs, etc. A recent work has also leveraged submodular functions to propose submodular information measures which have been found to be very useful in solving the problems of guided subset selection and guided summarization. In this work, we present Submodlib which is an open-source, easy-to-use, efficient and scalable Python library for submodular optimization with a C++ optimization engine. Submodlib finds its application in summarization, data subset selection, hyper parameter tuning, efficient training and more. Through a rich API, it offers a great deal of flexibility in the way it can be used. Source of Submodlib is available at https://github.com/decile-team/submodlib.