2 papers
stat.ML2025
Statistical Guarantees for High-Dimensional Stochastic Gradient Descent
Jiaqi Li, Zhipeng Lou, Johannes Schmidt-Hieber +1
Stochastic Gradient Descent (SGD) and its Ruppert-Polyak averaged variant (ASGD) lie at the heart of modern large-scale learning, yet their theoretical properties in high-dimension…
stat.ML2025
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
Xinchen Du, Wanrong Zhu, Wei Biao Wu +1
Constrained stochastic nonlinear optimization problems have attracted significant attention for their ability to model complex real-world scenarios in physics, economics, and biolo…