AdaMSS: Adaptive Multi-Subspace Approach for Parameter-Efficient Fine-Tuning
Published in NeurIPS 2025, 2025
Jingjing Zheng, Wanglong Lu, Yiming Dong, Chaojie Ji, Yankai Cao, Zhouchen Lin
Brief description:
AdaMSS is an adaptive multi-subspace approach for parameter-efficient fine-tuning, designed to improve the expressiveness-efficiency trade-off when adapting large models.
Recommended citation:
@inproceedings{zheng2025adamss,
title={AdaMSS: Adaptive Multi-Subspace Approach for Parameter-Efficient Fine-Tuning},
author={Zheng, Jingjing and Lu, Wanglong and Dong, Yiming and Ji, Chaojie and Cao, Yankai and Lin, Zhouchen},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
}