# Empirical Bayes approach to Truth Discovery problems

arXiv: https://arxiv.org/abs/2206.04816 · HTML: https://mlsanity.com/paper/2206.04816/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 1 of 1)

- [A Survey on Truth Discovery](https://mlsanity.com/paper/1505.02463/) (2015)

## Built on by (most cited first, 2 of 2)

- [Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks](https://mlsanity.com/paper/2208.00461/) (2022)
- [Causal Discovery Should Embrace the Wisdom of the Crowd](https://mlsanity.com/paper/2603.02678/) (2026)

## Abstract (arXiv)

When aggregating information from conflicting sources, one's goal is to find the truth. Most real-value truth discovery (TD) algorithms try to achieve this goal by estimating the competence of each source and then aggregating the conflicting information by weighing each source's answer proportionally to her competence. However, each of those algorithms requires more than a single source for such estimation and usually does not consider different estimation methods other than a weighted mean. Therefore, in this work we formulate, prove, and empirically test the conditions for an Empirical Bayes Estimator (EBE) to dominate the weighted mean aggregation. Our main result demonstrates that EBE, under mild conditions, can be used as a second step of any TD algorithm in order to reduce the expected error.