no code implementations • 19 Apr 2025 • Yifan Wang, Jie Gui, Xinli Shi, Linqing Gui, Yuan Yan Tang, James Tin-Yau Kwok
Unlike previous cancelable template generation schemes, ColorVein does not destroy the original biometric features and introduces additional color information to grayscale vein images.
no code implementations • 17 Apr 2025 • Yuan Zhou, Xinli Shi, Xuelong Li, Jiachen Zhong, Guanghui Wen, Jinde Cao
Employing DFL methods to solve such general optimization problems leads to the formulation of Decentralized Nonconvex Composite Federated Learning (DNCFL), a topic that remains largely underexplored.
no code implementations • 16 Apr 2025 • Yuan Zhou, Jiachen Zhong, Xinli Shi, Guanghui Wen, Xinghuo Yu
To overcome these limitations, we propose a novel composite federated learning algorithm called \textbf{FedCanon}, designed to solve the optimization problems comprising a possibly non-convex loss function and a weakly convex, potentially non-smooth regularization term.
no code implementations • 26 Sep 2024 • Chengze Jiang, Junkai Wang, Minjing Dong, Jie Gui, Xinli Shi, Yuan Cao, Yuan Yan Tang, James Tin-Yau Kwok
Based on the analysis, we mainly attribute the observed misalignment and disparity to the imbalanced optimization in FAT, which motivates us to optimize different training data adaptively to enhance robustness.
no code implementations • 22 Jul 2024 • Jie Gui, Chengze Jiang, Minjing Dong, Kun Tong, Xinli Shi, Yuan Yan Tang, DaCheng Tao
However, FAT suffers from catastrophic overfitting, which leads to a performance drop compared with multi-step adversarial training.
no code implementations • 19 Dec 2023 • Luyao Guo, Luqing Wang, Xinli Shi, Jinde Cao
In this paper, we propose a communication-efficient method MG-Skip with probabilistic local updates and multi-gossip communications for decentralized composite (smooth + nonsmooth) optimization, whose stepsize is independent of the number of local updates and the network topology.
no code implementations • 11 May 2023 • Zhuoxuan Li, Iakov Korovin, Xinli Shi, Sergey Gorbachev, Nadezhda Gorbacheva, Wei Huang, Jinde Cao
Rutting of asphalt pavements is a crucial design criterion in various pavement design guides.
no code implementations • 7 Feb 2023 • Luyao Guo, Xinli Shi, Jinde Cao, ZiHao Wang
The proposed algorithm uses uncoordinated network-independent constant stepsizes and only needs to approximately solve a sequence of proximal mappings, which is advantageous for solving decentralized composite optimization problems where the proximal mappings of the nonsmooth loss functions may not have analytical solutions.
no code implementations • 6 Dec 2022 • Luyao Guo, Jinde Cao, Xinli Shi, Shaofu Yang
In this paper, we propose a novel primal-dual proximal splitting algorithm (PD-PSA), named BALPA, for the composite optimization problem with equality constraints, where the loss function consists of a smooth term and a nonsmooth term composed with a linear mapping.
no code implementations • 5 Sep 2022 • Luyao Guo, Xinli Shi, Shaofu Yang, Jinde Cao
In this paper, we propose a novel Dual Inexact Splitting Algorithm (DISA) for distributed convex composite optimization problems, where the local loss function consists of a smooth term and a possibly nonsmooth term composed with a linear mapping.