3 papers
cs.IR2026
ASPIRE: Make Spectral Graph Collaborative Filtering Great Again via Adaptive Filter Learning
Yunhang He, Cong Xu, Zhangchi Zhu +2
Graph filter design is central to spectral collaborative filtering, yet most existing methods rely on manually tuned hyperparameters rather than fully learnable filters. We show th…
cs.LG2025
Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics
Cong Xu, Wenbin Liang, Mo Yu +7
The rapid scaling of models has led to prohibitively high training and fine-tuning costs. A major factor accounting for memory consumption is the widespread use of stateful optimiz…
cs.LG2025
CAdam: Confidence-Based Optimization for Online Learning
Shaowen Wang, Anan Liu, Jian Xiao +9
Modern recommendation systems frequently employ online learning to dynamically update their models with freshly collected data. The most commonly used optimizer for updating neural…