6 citations · 10 across the 10 of their papers we have counts for
5 papers · 1 filter
Revisiting Stochastic Gradient Descent for Strongly Convex Objectives: Tight Uniform-in-Time Bounds
Kang Chen, Yasong Feng, Tianyu Wang
Stochastic optimization via Stochastic Gradient Descent (SGD) is a fundamental problem in statistics and optimization. This paper revisits Stochastic Gradient Descent (SGD) for str…
Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods
Yasong Feng, Yifan Jiang, Tianyu Wang +1
This work provides a novel convergence analysis for stochastic optimization in terms of stopping times, addressing the practical reality that algorithms are often terminated adapti…
The Anytime Convergence of Stochastic Gradient Descent with Momentum: From a Continuous-Time Perspective
Yasong Feng, Yifan Jiang, Tianyu Wang +1
We study the stochastic optimization problem from a continuous-time perspective, with a focus on the Stochastic Gradient Descent with Momentum (SGDM) method. We show that the traje…
Convergence Rates of Stochastic Zeroth-order Gradient Descent for Łojasiewicz Functions
Tianyu Wang, Yasong Feng
We prove convergence rates of Stochastic Zeroth-order Gradient Descent (SZGD) algorithms for Lojasiewicz functions. The SZGD algorithm iterates as \begin{align*} \mathbf{x}_{t+1} =…
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective
Yasong Feng, Weiguo Gao
Nesterov's accelerated gradient method (NAG) is widely used in problems with machine learning background including deep learning, and is corresponding to a continuous-time differen…