24 citations · 87 across the 18 of their papers we have counts for
19 papers
Simpler Gradient Methods for Blind Super-Resolution with Lower Iteration Complexity
Jinsheng Li, Wei Cui, Xu Zhang
We study the problem of blind super-resolution, which can be formulated as a low-rank matrix recovery problem via vectorized Hankel lift (VHL). The previous gradient descent method…
Multi-Agent Continuous Control with Generative Flow Networks
Shuang Luo, Yinchuan Li, Shunyu Liu +3
Generative Flow Networks (GFlowNets) aim to generate diverse trajectories from a distribution in which the final states of the trajectories are proportional to the reward, serving…
Fast networked data selection via distributed smoothed quantile estimation
Xu Zhang, Marcos M. Vasconcelos
Collecting the most informative data from a large dataset distributed over a network is a fundamental problem in many fields, including control, signal processing and machine learn…
Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization
Jinsheng Li, Wei Cui, Xu Zhang
Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix. However, previous…
Meta Generative Flow Networks with Personalization for Task-Specific Adaptation
Xinyuan Ji, Xu Zhang, Wei Xi +3
Multi-task reinforcement learning and meta-reinforcement learning have been developed to quickly adapt to new tasks, but they tend to focus on tasks with higher rewards and more fr…
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
Xu Zhang, Wenpeng Li, Yunfeng Shao +3
Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients. However, FL suffers performance degradation from…
Robust one-shot estimation over shared networks in the presence of denial-of-service attacks
Xu Zhang, Marcos M. Vasconcelos
Multi-agent systems often communicate over low-power shared wireless networks in unlicensed spectrum, prone to denial-of-service attacks. We consider the following scenario: multip…
Top-k data selection via distributed sample quantile inference
Xu Zhang, Marcos Vasconcelos
We consider the problem of determining the top- largest measurements from a dataset distributed among a network of agents with noisy communication links. We show that this s…
Personalized Federated Learning via Variational Bayesian Inference
Xu Zhang, Yinchuan Li, Wenpeng Li +2
Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients. To address these challenges, this paper proposes a…
Data-Time Tradeoffs for Optimal k-Thresholding Algorithms in Compressed Sensing
Jialiang Xu, Xu Zhang
Optimal -thresholding algorithms are a class of -sparse signal recovery algorithms that overcome the shortcomings of traditional hard thresholding algorithms caused by the os…
Sparse Personalized Federated Learning
Xiaofeng Liu, Yinchuan Li, Qing Wang +3
Federated Learning (FL) is a collaborative machine learning technique to train a global model without obtaining clients' private data. The main challenges in FL are statistical div…
Spectrally Sparse Signal Recovery via Hankel Matrix Completion with Prior Information
Xu Zhang, Yulong Liu, Wei Cui
This paper studies the problem of reconstructing spectrally sparse signals from a small random subset of time domain samples via low-rank Hankel matrix completion with the aid of p…
Distributed remote estimation over the collision channel with and without local communication
Xu Zhang, Marcos M. Vasconcelos, Wei Cui +1
The emergence of the Internet-of-Things and cyber-physical systems necessitates the coordination of access to limited communication resources in an autonomous and distributed fashi…
Matrix Completion with Prior Subspace Information via Maximizing Correlation
Xu Zhang, Wei Cui, Yulong Liu
This paper studies the problem of completing a low-rank matrix from a few of its random entries with the aid of prior information. We suggest a strategy to incorporate prior inform…
Covariance Matrix Estimation from Correlated Sub-Gaussian Samples
Xu Zhang, Wei Cui, Yulong Liu
This paper studies the problem of estimating a covariance matrix from correlated sub-Gaussian samples. We consider using the correlated sample covariance matrix estimator to approx…
Compressive Multidimensional Harmonic Retrieval with Prior Knowledge
Yinchuan Li, Xu Zhang, Zegang Ding +1
This paper concerns the problem of estimating multidimensional (MD) frequencies using prior knowledge of the signal spectral sparsity from partial time samples. In many application…
Covariance Matrix Estimation from Linearly-Correlated Gaussian Samples
Wei Cui, Xu Zhang, Yulong Liu
Covariance matrix estimation concerns the problem of estimating the covariance matrix from a collection of samples, which is of extreme importance in many applications. Classical r…
Recovery of Structured Signals with Prior Information via Maximizing Correlation
Xu Zhang, Wei Cui, Yulong Liu
This paper considers the problem of recovering a structured signal from a relatively small number of noisy measurements with the aid of a similar signal which is known beforehand.…
Compressed Sensing with Prior Information via Maximizing Correlation
Xu Zhang, Wei Cui, Yulong Liu
Compressed sensing (CS) with prior information concerns the problem of reconstructing a sparse signal with the aid of a similar signal which is known beforehand. We consider a new…