14 citations · 15 across the 14 of their papers we have counts for
16 papers
Hybrid supervised instance segmentation by learning label noise suppression
Linwei Chen, Ying Fu, Shaodi You +1
To reach top accuracy, current fully supervised instance segmentation methods severely rely on large-scale pixel-wise labeled datasets. They are usually expensive and time-consumin…
Policy Iteration Is Not Strongly Polynomial for Deterministic Markov Decision Processes: The Price of Algorithmic Anarchy
Han Zhong, Yinyu Ye
We establish exponential iteration lower bounds in the number of states for Howard's policy iteration on deterministic discounted and average-reward Markov decision processes (MDPs…
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…
Real-Time Reconstruction of 3D Bone Models via Very-Low-Dose Protocols
Yiqun Lin, Haoran Sun, Yongqing Li +12
Patient-specific bone models are essential for designing surgical guides and preoperative planning, as they enable the visualization of intricate anatomical structures. However, tr…
Quantum Algorithms for Bandits with Knapsacks with Improved Regret and Time Complexities
Yuexin Su, Ziyi Yang, Peiyuan Huang +2
Bandits with knapsacks (BwK) constitute a fundamental model that combines aspects of stochastic integer programming with online learning. Classical algorithms for BwK with a time h…
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…
Wait-Less Offline Tuning and Re-solving for Online Decision Making
Jingruo Sun, Wenzhi Gao, Ellen Vitercik +1
Online linear programming (OLP) has found broad applications in revenue management and resource allocation. State-of-the-art OLP algorithms achieve low regret by repeatedly solving…
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…
Online Linear Programming with Batching
Haoran Xu, Peter W. Glynn, Yinyu Ye
We study Online Linear Programming (OLP) with batching. The planning horizon is cut into batches, and decisions on orders can be delayed to the end of their associated batch. T…
Adam-mini: Use Fewer Learning Rates To Gain More
Yushun Zhang, Congliang Chen, Ziniu Li +6
We propose Adam-mini, an optimizer that achieves on par or better performance than AdamW with 50% less memory footprint. Adam-mini reduces memory by cutting down the learning rate…
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…