Sample Quality Heterogeneity-Aware Federated Causal Discovery Through Adaptive Variable Space Selection
Abstract
We study a budget aggregation setting where voters express their preferred allocation of a fixed budget over a set of alternatives, and a mechanism aggregates these preferences into a single output allocation. Motivated by scenarios in which the budget is not perfectly divisible, we depart from the prevailing literature by restricting the mechanism to output allocations that assign integral amounts. This seemingly minor deviation has significant implications for the existence of truthful mechanisms. Specifically, when voters can propose fractional allocations, we demonstrate that the Gibbard-Satterthwaite theorem can be extended to our setting. In contrast, when voters are restricted to integral ballots, we identify a class of truthful mechanisms by adapting moving-phantom mechanisms to our context. Finally, we show that while a weak form of proportionality can be achieved alongside truthfulness, stronger proportionality notions derived from approval-based committee voting are incompatible with truthfulness.
Cite
Text
Guo et al. "Sample Quality Heterogeneity-Aware Federated Causal Discovery Through Adaptive Variable Space Selection." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/450Markdown
[Guo et al. "Sample Quality Heterogeneity-Aware Federated Causal Discovery Through Adaptive Variable Space Selection." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/guo2024ijcai-sample/) doi:10.24963/ijcai.2024/450BibTeX
@inproceedings{guo2024ijcai-sample,
title = {{Sample Quality Heterogeneity-Aware Federated Causal Discovery Through Adaptive Variable Space Selection}},
author = {Guo, Xianjie and Yu, Kui and Wang, Hao and Cui, Lizhen and Yu, Han and Li, Xiaoxiao},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {4071-4079},
doi = {10.24963/ijcai.2024/450},
url = {https://mlanthology.org/ijcai/2024/guo2024ijcai-sample/}
}