# Aggregating Predictions on Multiple Non-disclosed Datasets using Conformal Prediction

arXiv: https://arxiv.org/abs/1806.04000 · HTML: https://mlsanity.com/paper/1806.04000/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

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

- [Distribution-Free Federated Learning with Conformal Predictions](https://mlsanity.com/paper/2110.07661/) (2021)

## Abstract (arXiv)

Conformal Prediction is a machine learning methodology that produces valid prediction regions under mild conditions. In this paper, we explore the application of making predictions over multiple data sources of different sizes without disclosing data between the sources. We propose that each data source applies a transductive conformal predictor independently using the local data, and that the individual predictions are then aggregated to form a combined prediction region. We demonstrate the method on several data sets, and show that the proposed method produces conservatively valid predictions and reduces the variance in the aggregated predictions. We also study the effect that the number of data sources and size of each source has on aggregated predictions, as compared with equally sized sources and pooled data.