apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html

Summarize Massive Datasets using Submodular Optimization

Submodular optimization for context engineering: query fan-out, text selection, passage reranking

visual-inertial odometry (VINS-Mono) with motion-aware feature selection

NAACL 2019: Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation

A collection of optimization algorithms for maximizing unconstrained submodular set functions.

(IJCAI 2019) Submodular Batch Selection for Training Deep Neural Networks

Code for the paper: Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization

PyEDCR is a metacognitive neuro-symbolic method for learning error detection and correction rules in deployed ML models using combinatorial sub-modular set optimization

Memory Efficient Max Flow

Recommending movies based on a utility function of movie ratings

Efficient greedy-based methods for constrained submodular optimization.

Greedy and Lazy Greedy Sub Modular Optimisation

Some tools for submodular function minimization in Python

A GPU accelerated Submodular Optimization toolkit.

Code for our ICML '24 paper, "Submodular framework for structured-sparse optimal transport".

Probabilistic Gas Leak Rate Estimation using Submodular Function Maximization with Routing Constraints

Routines for submodular set function minimization

[AAAI 2023] Implementation of Submodular-Maximization-under-the-Intersection-of-Matroid-and-Knapsack-Constraints

Submodular Subset Selection for Long-Document Question Answering