89 citations · 315 across the 16 of their papers we have counts for
16 papers
Super-Resolution Harmonic Retrieval of Non-Circular Signals
Yu Zhang, Yue Wang, Zhi Tian +2
This paper proposes a super-resolution harmonic retrieval method for uncorrelated strictly non-circular signals, whose covariance and pseudo-covariance present Toeplitz and Hankel…
Distributed Swarm Learning for Internet of Things at the Edge: Where Artificial Intelligence Meets Biological Intelligence
Yue Wang, Zhi Tian, Xin Fan +3
With the proliferation of versatile Internet of Things (IoT) services, smart IoT devices are increasingly deployed at the edge of wireless networks to perform collaborative machine…
Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attacks
Guanqiang Zhou, Ping Xu, Yue Wang +1
In distributed learning systems, robustness issues may arise from two sources. On one hand, due to distributional shifts between training data and test data, the trained model coul…
Finite-Time Error Bounds for Greedy-GQ
Yue Wang, Yi Zhou, Shaofeng Zou
Greedy-GQ with linear function approximation, originally proposed in \cite{maei2010toward}, is a value-based off-policy algorithm for optimal control in reinforcement learning, and…
QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM
Ping Xu, Yue Wang, Xiang Chen +1
This paper focuses on online kernel learning over a decentralized network. Each agent in the network receives continuous streaming data locally and works collaboratively to learn a…
CB-DSL: Communication-efficient and Byzantine-robust Distributed Swarm Learning on Non-i.i.d. Data
Xin Fan, Yue Wang, Yan Huo +1
The valuable data collected by IoT devices in edge networks together with the resurgence of ML stimulate the latest trend of edge AI. However, recent FL methods face major challeng…
Online Robust Reinforcement Learning with Model Uncertainty
Yue Wang, Shaofeng Zou
Robust reinforcement learning (RL) is to find a policy that optimizes the worst-case performance over an uncertainty set of MDPs. In this paper, we focus on model-free robust RL, w…
BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers
Xin Fan, Yue Wang, Yan Huo +1
As a promising distributed learning technology, analog aggregation based federated learning over the air (FLOA) provides high communication efficiency and privacy provisioning unde…
Joint Optimization of Communications and Federated Learning Over the Air
Xin Fan, Yue Wang, Yan Huo +1
Federated learning (FL) is an attractive paradigm for making use of rich distributed data while protecting data privacy. Nonetheless, nonideal communication links and limited trans…
Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation
Yue Wang, Shaofeng Zou, Yi Zhou
Temporal-difference learning with gradient correction (TDC) is a two time-scale algorithm for policy evaluation in reinforcement learning. This algorithm was initially proposed wit…
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air
Xin Fan, Yue Wang, Yan Huo +1
For distributed learning among collaborative users, this paper develops and analyzes a communication-efficient scheme for federated learning (FL) over the air, which incorporates 1…
Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise
Yue Wang, Shaofeng Zou
Greedy-GQ is an off-policy two timescale algorithm for optimal control in reinforcement learning. This paper develops the first finite-sample analysis for the Greedy-GQ algorithm w…
COKE: Communication-Censored Decentralized Kernel Learning
Ping Xu, Yue Wang, Xiang Chen +1
This paper studies the decentralized optimization and learning problem where multiple interconnected agents aim to learn an optimal decision function defined over a reproducing ker…
Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems
Yue Wang, Yu Zhang, Zhi Tian +2
This paper develops efficient channel estimation techniques for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems under practical hardware limitations,…
Low-complexity optimization for Two-Dimensional Direction-of-arrival Estimation via Decoupled Atomic Norm Minimization
Zhi Tian, Zhe Zhang, Yue Wang
This paper presents an efficient optimization technique for super-resolution two-dimensional (2D) direction of arrival (DOA) estimation by introducing a new formulation of atomic n…
Efficient Two-Dimensional Line Spectrum Estimation Based on Decoupled Atomic Norm Minimization
Zhe Zhang, Yue Wang, Zhi Tian
This paper presents an efficient optimization technique for gridless {2-D} line spectrum estimation, named decoupled atomic norm minimization (D-ANM). The framework of atomic norm…