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

Recommending movies based on a utility function of movie ratings

Memory Efficient Max Flow

Efficient greedy-based methods for constrained submodular optimization.

Greedy and Lazy Greedy Sub Modular Optimisation

Some tools for submodular function minimization in Python

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

A GPU accelerated Submodular Optimization toolkit.

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

Routines for submodular set function minimization

InSQuaD is a research framework for efficient in-context learning that leverages submodular mutual information to optimize the quality-diversity tradeoff in example selection for large language models

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