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cs.LG2026
Stochastic Gradient Descent with Momentum is Algorithmically Stable
Yunwen Lei, Zimeng Wang, Xiaoming Yuan
Stochastic gradient descent with momentum (SGDM) is one of the most widely used optimization algorithms in machine learning. While optimization properties of SGDM have been extensi…
cs.LG2026
Learning Theory of the SVRG: Generalization and Convergence Analysis
Yunwen Lei, Zimeng Wang, Xiaoming Yuan
Variance reduction (VR) methods employ stochastic gradients with decreasing variance, and they have been widely applied to solve large-scale optimization problems in machine learni…
cs.LG2025
BestServe: Serving Strategies with Optimal Goodput in Collocation and Disaggregation Architectures
Xiannan Hu, Tianyou Zeng, Xiaoming Yuan +3
Serving large language models (LLMs) to millions of users requires efficient resource allocation and parallelism strategies. It is a labor intensive trial-and-error process to find…