3 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
Central Limit Theorems for Stochastic Gradient Descent Quantile Estimators
Ziyang Wei, Jiaqi Li, Likai Chen +1
This paper develops asymptotic theory for quantile estimation via stochastic gradient descent (SGD) with a constant learning rate. The quantile loss function is neither smooth nor…
stat.ML2024
Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models
Jiaqi Li, Johannes Schmidt-Hieber, Wei Biao Wu
This paper proposes an asymptotic theory for online inference of the stochastic gradient descent (SGD) iterates with dropout regularization in linear regression. Specifically, we e…