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
Submodular optimization for context engineering: query fan-out, text selection, passage reranking
A collection of optimization algorithms for maximizing unconstrained submodular set functions.
Personalized knowledge graph summarization based on historical queries
Non-Uniform Subset Selection for Active Learning in Structured Data
A GPU accelerated Submodular Optimization toolkit.
Probabilistic Gas Leak Rate Estimation using Submodular Function Maximization with Routing Constraints
Routines for submodular set function minimization
Code for our AISTATS '22 paper: Improving Attribution Methods by Learning Submodular Functions.
A partial Benders decomposition (PBD) for solving choice-based competitive facility location problems (CBCFLP)
delta-submodular work codes
Active learning & submodularity: selecting an informative subset of training data and comparing classifier accuracy (KNN, Logistic Regression, SVM, Random Forest) on the subset vs. the full dataset.
Randomized Greedy Learning Under Full-bandit Feedback
Another blog, another platform
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