# Deep Sensitivity Analysis for Objective-Oriented Combinatorial Optimization

arXiv: https://arxiv.org/abs/2403.00016 · HTML: https://mlsanity.com/paper/2403.00016/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Survey for Solving Mixed Integer Programming via Machine Learning](https://mlsanity.com/paper/2203.02878/) (2022)
- [Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap](https://mlsanity.com/paper/1711.11286/) (2017)
- [Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch](https://mlsanity.com/paper/2206.06965/) (2022)
- [Bayesian Optimization-based Combinatorial Assignment](https://mlsanity.com/paper/2208.14698/) (2022)
- [Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population](https://mlsanity.com/paper/1905.10684/) (2019)
- [Variance-based sensitivity analysis for weighting estimators result in more informative bounds](https://mlsanity.com/paper/2208.01691/) (2022)
- [Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization](https://mlsanity.com/paper/2205.13948/) (2022)
- [Trainable Noise Model as an XAI evaluation method: application on Sobol for remote sensing image segmentation](https://mlsanity.com/paper/2310.01828/) (2023)

## Built on by (most cited first, 1 of 1)

- [Bayesian-Guided Generation of Synthetic Microbiomes with Minimized Pathogenicity](https://mlsanity.com/paper/2405.00070/) (2024)

## Abstract (arXiv)

Pathogen control is a critical aspect of modern poultry farming, providing important benefits for both public health and productivity. Effective poultry management measures to reduce pathogen levels in poultry flocks promote food safety by lowering risks of food-borne illnesses. They also support animal health and welfare by preventing infectious diseases that can rapidly spread and impact flock growth, egg production, and overall health. This study frames the search for optimal management practices that minimize the presence of multiple pathogens as a combinatorial optimization problem. Specifically, we model the various possible combinations of management settings as a solution space that can be efficiently explored to identify configurations that optimally reduce pathogen levels. This design incorporates a neural network feedback-based method that combines feature explanations with global sensitivity analysis to ensure combinatorial optimization in multiobjective settings. Our preliminary experiments have promising results when applied to two real-world agricultural datasets. While further validation is still needed, these early experimental findings demonstrate the potential of the model to derive targeted feature interactions that adaptively optimize pathogen control under varying real-world constraints.