4 papers
Dimension-Adaptive Batched Lipschitz Narrowing Without Knowing the Zooming Dimension
Yasong Feng
The Appropriately Combined Edge-length (ACE) sequence in A-BLiN depends on the zooming dimension . This note removes that dependence. The next edge length is selected from the…
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…