3 papers
cs.DC2026
Communication-Avoiding Linear Algebraic Kernel K-Means on GPUs
Julian Bellavita, Matthew Rubino, Nakul Iyer +4
Clustering is an important tool in data analysis, with K-means being popular for its simplicity and versatility. However, it cannot handle non-linearly separable clusters. Kernel K…
stat.ML2025
Enhanced Cyclic Coordinate Descent Methods for Elastic Net Penalized Linear Models
Yixiao Wang, Zishan Shao, Ting Jiang +1
We present a novel enhanced cyclic coordinate descent (ECCD) framework for solving generalized linear models with elastic net constraints that reduces training time in comparison t…
cs.DC2024
Scalable Dual Coordinate Descent for Kernel Methods
Zishan Shao, Aditya Devarakonda
Dual Coordinate Descent (DCD) and Block Dual Coordinate Descent (BDCD) are important iterative methods for solving convex optimization problems. In this work, we develop scalable D…