4 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.IT2023
A Generalized Nyquist-Shannon Sampling Theorem Using the Koopman Operator
Zhexuan Zeng, Jun Liu, Ye Yuan
In the field of signal processing, the sampling theorem plays a fundamental role for signal reconstruction as it bridges the gap between analog and digital signals. Following the c…
cs.LG2023
Almost Sure Saddle Avoidance of Stochastic Gradient Methods without the Bounded Gradient Assumption
Jun Liu, Ye Yuan
We prove that various stochastic gradient descent methods, including the stochastic gradient descent (SGD), stochastic heavy-ball (SHB), and stochastic Nesterov's accelerated gradi…
cs.LG2022★ 4 cited
On Almost Sure Convergence Rates of Stochastic Gradient Methods
Jun Liu, Ye Yuan
The vast majority of convergence rates analysis for stochastic gradient methods in the literature focus on convergence in expectation, whereas trajectory-wise almost sure convergen…