# Harnessing The Collective Wisdom: Fusion Learning Using Decision Sequences From Diverse Sources

arXiv: https://arxiv.org/abs/2308.11026 · HTML: https://mlsanity.com/paper/2308.11026/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2023.

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

- [Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency -- A Review](https://mlsanity.com/paper/2311.11159/) (2023)

## Abstract (arXiv)

We introduce an Integrative Ranking and Thresholding (IRT) framework for fusing evidence from multiple testing procedures. The key innovation is a method that transforms binary testing decisions into compound e-values, enabling the combination of findings across diverse data sources or studies. We demonstrate that IRT ensures overall false discovery rate (FDR) control, provided the individual studies maintain their respective FDR levels. This approach is highly flexible and is a powerful alternative for fusing inferences in meta-analysis where some studies report summary statistics while the rest reveal only the rejections under a pre-specified FDR level. Extensions to alternative Type I error control measures are explored.