Core-Structures-Guided Multi-Modal Classification Neural Architecture Search
Abstract
We consider the computation for allocations of indivisible chores that are approximately EFX and fractional Pareto optimal (fPO). It has been shown that 3-EFX and fPO allocations for bi-valued instances always exist, where the cost of an item to an agent is either 1 or k (where k > 1), by rounding the (fractional) earning restricted equilibrium. In this work, we improve the approximation ratio to (2-1/k), while preserving the fractional Pareto optimality. Instead of rounding fractional equilibrium, our algorithm starts with the integral EF1 equilibrium for bi-valued chores and reallocates items until approximate EFX is achieved. We further improve our result for the case when k=2 and devise an algorithm that computes EFX and fPO allocations.
Cite
Text
Fu et al. "Core-Structures-Guided Multi-Modal Classification Neural Architecture Search." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/440Markdown
[Fu et al. "Core-Structures-Guided Multi-Modal Classification Neural Architecture Search." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/fu2024ijcai-core/) doi:10.24963/ijcai.2024/440BibTeX
@inproceedings{fu2024ijcai-core,
title = {{Core-Structures-Guided Multi-Modal Classification Neural Architecture Search}},
author = {Fu, Pinhan and Liang, Xinyan and Luo, Tingjin and Guo, Qian and Zhang, Yayu and Qian, Yuhua},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {3980-3988},
doi = {10.24963/ijcai.2024/440},
url = {https://mlanthology.org/ijcai/2024/fu2024ijcai-core/}
}