8 papers
A Multi-Step Inertial ADMM Algorithm for Federated Learning
Hao Li, Qingsong Wang, Xihong Yan +2
Federated learning has recently emerged as an important paradigm for large-scale distributed model training, which can be naturally formulated as a class of distributed learning pr…
Composite and Stochastic Smoothed Decentralized Bregman Proximal-Gradient Methods
Shengze Xu, Chenhan Jin, Qingsong Wang +1
Smoothed decentralized Bregman proximal-gradient (SDBPG) methods combine relative-smoothness geometry with edge-dual consensus. We extend SDBPG to node-specific convex nonsmooth re…
OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
Chenhan Jin, Shengze Xu, Qingsong Wang +3
Data pruning (DP), as an oft-stated strategy to alleviate heavy training burdens, reduces the volume of training samples according to a well-defined pruning method while striving f…
An Accelerated Alternating Partial Bregman Algorithm for ReLU-based Matrix Decomposition
Qingsong Wang, Yunfei Qu, Chunfeng Cui +1
Despite the remarkable success of low-rank estimation in data mining, its effectiveness diminishes when applied to data that inherently lacks low-rank structure. To address this li…
Multi-step Inertial Accelerated Doubly Stochastic Gradient Methods for Block Term Tensor Decomposition
Zehui Liu, Qingsong Wang, Chunfeng Cui
In this paper, we explore a specific optimization problem that combines a differentiable nonconvex function with a nondifferentiable function for multi-block variables, which is pa…
Inertial Accelerated Stochastic Mirror Descent for Large-Scale Generalized Tensor CP Decomposition
Zehui Liu, Qingsong Wang, Chunfeng Cui +1
The majority of classic tensor CP decomposition models are designed for squared loss, employing Euclidean distance as a local proximal term. However, the Euclidean distance is unsu…
A Momentum Accelerated Algorithm for ReLU-based Nonlinear Matrix Decomposition
Qingsong Wang, Chunfeng Cui, Deren Han
Recently, there has been a growing interest in the exploration of Nonlinear Matrix Decomposition (NMD) due to its close ties with neural networks. NMD aims to find a low-rank matri…
A Bregman Proximal Stochastic Gradient Method with Extrapolation for Nonconvex Nonsmooth Problems
Qingsong Wang, Zehui Liu, Chunfeng Cui +1
In this paper, we explore a specific optimization problem that involves the combination of a differentiable nonconvex function and a nondifferentiable function. The differentiable…