Single-pass SGD with exponentially decaying steps is claimed to reach minimax-optimal excess risk in high-dimensional kernel regression for well-specified problems, with averaging handling misspecified problems.
Advani, Andrew M
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Learning Curves of Stochastic Gradient Descent in Kernel Regression
Single-pass SGD with exponentially decaying steps is claimed to reach minimax-optimal excess risk in high-dimensional kernel regression for well-specified problems, with averaging handling misspecified problems.