14 citations · 15 across the 14 of their papers we have counts for
9 papers · 1 filter
A Two-Layer Framework for Joint Online Configuration Selection and Admission Control
Owen Shen, Haoran Xu, Yinyu Ye +2
We study online configuration selection with admission control problem, which arises in LLM serving, GPU scheduling, and revenue management. In a planning horizon with periods,…
Gradient Methods with Online Scaling Part II. Practical Aspects
Ya-Chi Chu, Wenzhi Gao, Yinyu Ye +1
Part I of this work [Gao25] establishes online scaled gradient methods (OSGM), a framework that utilizes online convex optimization to adapt stepsizes in gradient methods. This pap…
Adaptive Inertial Method
Han Long, Bingsheng He, Yinyu Ye +1
In this paper, we introduce the Adaptive Inertial Method (AIM), a novel framework for accelerated first-order methods through a customizable inertial term. We provide a rigorous co…
Gradient Methods with Online Scaling Part I. Theoretical Foundations
Wenzhi Gao, Ya-Chi Chu, Yinyu Ye +1
This paper establishes the theoretical foundations of the online scaled gradient methods (OSGM), a framework that utilizes online learning to adapt stepsizes and provably accelerat…
Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent
Ya-Chi Chu, Wenzhi Gao, Yinyu Ye +1
This paper investigates the convergence properties of the hypergradient descent method (HDM), a 25-year-old heuristic originally proposed for adaptive stepsize selection in stochas…
When Does Primal Interior Point Method Beat Primal-dual in Linear Optimization?
Wenzhi Gao, Huikang Liu, Yinyu Ye +1
The primal-dual interior point method (IPM) is widely regarded as the most efficient IPM variant for linear optimization. In this paper, we demonstrate that the improved stability…
Orientation Determination of Cryo-EM Images Using Block Stochastic Riemannian Subgradient Methods
Wanyu Zhang, Ruili Gou, Huikang Liu +2
The determination of molecular orientations is crucial for the three-dimensional reconstruction of Cryo-EM images. Traditionally addressed using the common-line method, this challe…
Gradient Methods with Online Scaling
Wenzhi Gao, Ya-Chi Chu, Yinyu Ye +1
We introduce a framework to accelerate the convergence of gradient-based methods with online learning. The framework learns to scale the gradient at each iteration through an onlin…
How a Small Amount of Data Sharing Benefits Distributed Optimization and Learning : The Upside of Data Heterogeneity
Mingxi Zhu, Yinyu Ye
Distributed optimization algorithms are widely used in machine learning. This paper investigates how a small amount of data sharing can improve their performance. Focusing on gener…