3 papers
cs.LG2026
Sketched Linear Contrastive Learning: Approximation, Optimization, and Statistical Scaling
Ziyan Chen, Zhongzhu Zhou, Ding-Xuan Zhou
Scaling laws describe how learning performance varies with model size, data size, and compute. While recent theoretical work has established scaling laws for sketched linear regres…
cs.LG2026
Scaling Laws for Dynamic Mini-Batch SGD in Sketched Linear Regression
Ziyan Chen, Zhongzhu Zhou, Ding-Xuan Zhou
Mini-batching is central to large-scale optimization, yet its role in statistical scaling laws remains limited. We study one-pass and multi-pass batch SGD for sketched linear regre…
cs.LG2026
Two-Stage Data Synthesization: A Statistics-Driven Restricted Trade-off between Privacy and Prediction
Xiaotong Liu, Shao-Bo Lin, Jun Fan +1
Synthetic data have gained increasing attention across various domains, with a growing emphasis on their performance in downstream prediction tasks. However, most existing synthesi…