# A decision-theoretic model for a principal-agent collaborative learning problem

arXiv: https://arxiv.org/abs/2409.16068 · HTML: https://mlsanity.com/paper/2409.16068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 24 Sep 2024.

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

- [A naive aggregation algorithm for improving generalization in a class of learning problems](https://mlsanity.com/paper/2409.04352/) (2024)

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

- [A brief note on learning problem with global perspectives](https://mlsanity.com/paper/2601.05441/) (2026)

## Abstract (arXiv)

In this technical note, we consider a collaborative learning framework with principal-agent setting, in which the principal at each time-step determines a set of appropriate aggregation coefficients based on how the current parameter estimates from a group of K agents effectively performed in connection with a separate test dataset, which is not part of the agents' training model datasets. Whereas, the agents, who act together as a team, then update their parameter estimates using a discrete-time version of Langevin dynamics with mean-field-like interaction term, but guided by their respective different training model datasets. Here, we propose a decision-theoretic framework that explicitly describes how the principal progressively determines a set of nonnegative and sum to one aggregation coefficients used by the agents in their mean-field-like interaction term, that eventually leading them to reach a consensus optimal parameter estimate. Interestingly, due to the inherent feedbacks and cooperative behavior among the agents, the proposed framework offers some advantages in terms of stability and generalization, despite that both the principal and the agents do not necessarily need to have any knowledge of the sample distributions or the quality of each others' datasets.