3 citations · 3 across the 4 of their papers we have counts for
5 papers
Agnostic Smoothed Online Regression with Adversarial Responses
Xuanyu Chen, Yue Yu
We study smoothed online prediction with bounded adversarial responses. This widely studied framework bridges i.i.d. sampling and adversarial covariate selection through a smoothne…
Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data
Xuanyu Chen, Nan Yang, Shuai Wang +1
Recent research has introduced distributed self-supervised learning (D-SSL) approaches to leverage vast amounts of unlabeled decentralized data. However, D-SSL faces the critical c…
Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?
Xuanyu Chen, Nan Yang, Shuai Wang +1
The recent success of large language models (LLMs) has sparked a growing interest in training large-scale models. As the model size continues to scale, concerns are growing about t…
Memory-Efficient Fine-Tuning via Low-Rank Activation Compression
Jiang-Xin Shi, Wen-Da Wei, Jin-Fei Qi +3
The parameter-efficient fine-tuning paradigm has garnered significant attention with the advancement of foundation models. Although numerous methods have been proposed to reduce th…
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder
Nan Yang, Xuanyu Chen, Charles Z. Liu +3
Latest federated learning (FL) methods started to focus on how to use unlabeled data in clients for training due to users' privacy concerns, high labeling costs, or lack of experti…