Ziyang Wei — accepted AI-conference papers

2025

Asymptotic theory of SGD with a general learning-rate

Or Goldreich, Ziyang Wei, SOHAM BONNERJEE, Jiaqi Li, Wei Biao Wu

NeurIPS 2025poster

Stochastic gradient descent (SGD) with polynomially decaying step‐sizes has long underpinned theoretical analyses, yielding a broad spectrum of statistically attractive guarantees. Yet in practice, such schedules find rare use due to their prohibitively slow convergence, revealing a persistent gap b…

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2025

Gaussian Approximation and Concentration of Constant Learning-Rate Stochastic Gradient Descent

Ziyang Wei, Jiaqi Li, Zhipeng Lou, Wei Biao Wu

NeurIPS 2025poster

We establish a comprehensive finite-sample and asymptotic theory for stochastic gradient descent (SGD) with constant learning rates. First, we propose a novel linear approximation technique to provide a quenched central limit theorem (CLT) for SGD iterates with refined tail properties, showing that…

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