2025-01-14-yamamoto25a.md

title	booktitle	year	volume	series	month	publisher	pdf	url	software	openreview	abstract	layout	issn	id	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	address	container-title	genre	issued	extras
Fast Stealthily Biased Sampling Using Sliced Wasserstein Distance	Proceedings of the 16th Asian Conference on Machine Learning	2025	260	Proceedings of Machine Learning Research	0	PMLR	https://raw.githubusercontent.com/mlresearch/v260/main/assets/yamamoto25a/yamamoto25a.pdf	https://proceedings.mlr.press/v260/yamamoto25a.html	https://github.com/yyd-Yudai/FastSBS	0ymcR0F1wP	Ensuring fairness is essential when implementing machine learning models in practical applications. However, recent research has revealed that benchmark datasets can be crafted as fake evidence of fairness from unfair models using a method called Stealthily Biased Sampling (SBS). SBS minimizes the Wasserstein distance to manipulate a fake benchmark so that the distribution of the benchmark closely resembles the true data distribution. This optimization requires superquadratic time relative to the dataset size, making SBS applicable only to small-sized datasets. In this study, we reveal for the first time that the risk of manipulated benchmark datasets exists even for large-sized datasets. This finding indicates the necessity of considering the potential for manipulated benchmarks regardless of their size. To demonstrate this risk, we developed FastSBS, a computationally efficient variant of SBS using the Sliced Wasserstein distance. FastSBS is optimized by a stochastic gradient-based method, which requires only nearly linear time for each update. In experiments with both synthetic and real-world datasets, we show that FastSBS is an order of magnitude faster than the original SBS for large datasets while maintaining the quality of the manipulated benchmark.	inproceedings	2640-3498	yamamoto25a	Fast Stealthily Biased Sampling Using Sliced Wasserstein Distance	873	888	873-888	873	false	Nguyen, Vu and Lin, Hsuan-Tien	given family Vu Nguyen given family Hsuan-Tien Lin	Yamamoto, Yudai and Hara, Satoshi	given family Yudai Yamamoto given family Satoshi Hara	2025-01-14		Proceedings of the 16th Asian Conference on Machine Learning	inproceedings	date-parts 2025 1 14	label link Supplementary PDF https://raw.githubusercontent.com/mlresearch/v260/main/assets/assets/yamamoto25a/yamamoto25a-supp.pdf