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20162026
most citedDistributed Algorithms for Composite Optimization: Unified Framework and Convergence Analysis

86 citations · 126 across the 36 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

eess.SP2024

On Random Sampling of Diffused Graph Signals with Sparse Inputs on Vertex Domain

Yingcheng Lai, Li Chai, Jinming Xu

The sampling of graph signals has recently drawn much attention due to the wide applications of graph signal processing. While a lot of efficient methods and interesting results ha…

cs.RO2024

Multi-robot autonomous 3D reconstruction using Gaussian splatting with Semantic guidance

Jing Zeng, Qi Ye, Tianle Liu +5

Implicit neural representations and 3D Gaussian splatting (3DGS) have shown great potential for scene reconstruction. Recent studies have expanded their applications in autonomous…

math.OC2024

Hessian-Free Distributed Bilevel Optimization via Penalization with Time-Scale Separation

Youcheng Niu, Jinming Xu, Ying Sun +2

This paper considers a class of distributed bilevel optimization (DBO) problems with a coupled inner-level subproblem. Existing approaches typically rely on hypergradient estimatio…

math.OC2024

Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes

Yan Huang, Xiang Li, Yipeng Shen +2

In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive steps…

cs.LG2024

PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds

Zehan Zhu, Yan Huang, Xin Wang +1

In this paper, we propose a differentially private decentralized learning method (termed PrivSGP-VR) which employs stochastic gradient push with variance reduction and guarantees $…

cs.RO2024

MAexp: A Generic Platform for RL-based Multi-Agent Exploration

Shaohao Zhu, Jiacheng Zhou, Anjun Chen +3

The sim-to-real gap poses a significant challenge in RL-based multi-agent exploration due to scene quantization and action discretization. Existing platforms suffer from the ineffi…

cs.RO2024

Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change

Ruichen Xu, Xiao Liu, Jinming Xu +1

Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomo…