1.7k citations · 5.2k across the 50 of their papers we have counts for
50 papers
Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning
Sagar Shrestha, Xiao Fu, Mingyi Hong
This work revisits the joint beamforming (BF) and antenna selection (AS) problem, as well as its robust beamforming (RBF) version under imperfect channel state information (CSI). S…
Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing
Meng Ding, Xiao Fu, Xi-Le Zhao
The block-term tensor decomposition model with multilinear rank- terms (or, the "LL1 tensor decomposition" in short) offers a valuable alternative for hyperspectral un…
Memory-Efficient Convex Optimization for Self-Dictionary Separable Nonnegative Matrix Factorization: A Frank-Wolfe Approach
Tri Nguyen, Xiao Fu, Ruiyuan Wu
Nonnegative matrix factorization (NMF) often relies on the separability condition for tractable algorithm design. Separability-based NMF is mainly handled by two types of approache…
Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder
Qi Lyu, Xiao Fu
This work focuses on the problem of unraveling nonlinearly mixed latent components in an unsupervised manner. The latent components are assumed to reside in the probability simplex…
Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization
Shahana Ibrahim, Xiao Fu
Unsupervised learning of the Dawid-Skene (D&S) model from noisy, incomplete and crowdsourced annotations has been a long-standing challenge, and is a critical step towards reliably…
Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective
Haoran Sun, Wenqiang Pu, Xiao Fu +2
There has been a growing interest in developing data-driven, and in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such…
Stochastic Block-ADMM for Training Deep Networks
Saeed Khorram, Xiao Fu, Mohamad H. Danesh +2
In this paper, we propose Stochastic Block-ADMM as an approach to train deep neural networks in batch and online settings. Our method works by splitting neural networks into an arb…
Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models
Sagar Shrestha, Xiao Fu, Mingyi Hong
The spectrum cartography (SC) technique constructs multi-domain (e.g., frequency, space, and time) radio frequency (RF) maps from limited measurements, which can be viewed as an il…
Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses
Wenqiang Pu, Shahana Ibrahim, Xiao Fu +1
This work considers low-rank canonical polyadic decomposition (CPD) under a class of non-Euclidean loss functions that frequently arise in statistical machine learning and signal p…
StatEcoNet: Statistical Ecology Neural Networks for Species Distribution Modeling
Eugene Seo, Rebecca A. Hutchinson, Xiao Fu +4
This paper focuses on a core task in computational sustainability and statistical ecology: species distribution modeling (SDM). In SDM, the occurrence pattern of a species on a lan…
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors
Yu-Chun Miao, Xi-Le Zhao, Xiao Fu +2
Image denoising is often empowered by accurate prior information. In recent years, data-driven neural network priors have shown promising performance for RGB natural image denoisin…
Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment
Haoran Sun, Wenqiang Pu, Minghe Zhu +3
There has been a growing interest in developing data-driven and in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such a…
Mixed Membership Graph Clustering via Systematic Edge Query
Shahana Ibrahim, Xiao Fu
This work considers clustering nodes of a largely incomplete graph. Under the problem setting, only a small amount of queries about the edges can be made, but the entire graph is n…
Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling
Meng Ding, Xiao Fu, Ting-Zhu Huang +2
This work revisits coupled tensor decomposition (CTD)-based hyperspectral super-resolution (HSR). HSR aims at fusing a pair of hyperspectral and multispectral images to recover a s…
Recovering Joint Probability of Discrete Random Variables from Pairwise Marginals
Shahana Ibrahim, Xiao Fu
Learning the joint probability of random variables (RVs) is the cornerstone of statistical signal processing and machine learning. However, direct nonparametric estimation for high…
Computing Large-Scale Matrix and Tensor Decomposition with Structured Factors: A Unified Nonconvex Optimization Perspective
Xiao Fu, Nico Vervliet, Lieven De Lathauwer +2
The proposed article aims at offering a comprehensive tutorial for the computational aspects of structured matrix and tensor factorization. Unlike existing tutorials that mainly fo…
On Recoverability of Randomly Compressed Tensors with Low CP Rank
Shahana Ibrahim, Xiao Fu, Xingguo Li
Our interest lies in the recoverability properties of compressed tensors under the \textit{canonical polyadic decomposition} (CPD) model. The considered problem is well-motivated i…
Spectrum Cartography via Coupled Block-Term Tensor Decomposition
Guoyong Zhang, Xiao Fu, Jun Wang +2
Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i.e., a radio map)---from limited samples taken spars…
Hyperspectral Super-resolution: A Coupled Nonnegative Block-term Tensor Decomposition Approach
Guoyong Zhang, Xiao Fu, Kejun Huang +1
Hyperspectral super-resolution (HSR) aims at fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image (SRI). Recently, a coupled tenso…
Link Prediction Under Imperfect Detection: Collaborative Filtering for Ecological Networks
Xiao Fu, Eugene Seo, Justin Clarke +1
Matrix completion based collaborative filtering is considered scalable and effective for online service link prediction (e.g., movie recommendation) but does not meet the challenge…
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
Shahana Ibrahim, Xiao Fu, Nikos Kargas +1
The data deluge comes with high demands for data labeling. Crowdsourcing (or, more generally, ensemble learning) techniques aim to produce accurate labels via integrating noisy, no…
Nonlinear Multiview Analysis: Identifiability and Neural Network-assisted Implementation
Qi Lyu, Xiao Fu
Multiview analysis aims at extracting shared latent components from data samples that are acquired in different domains, e.g., image, text, and audio. Classic multiview analysis, e…
Amplitude Retrieval for Channel Estimation of MIMO Systems with One-Bit ADCs
Cheng Qian, Xiao Fu, Nicholas D. Sidiropoulos
This letter revisits the channel estimation problem for MIMO systems with one-bit analog-to-digital converters (ADCs) through a novel algorithm--Amplitude Retrieval (AR). Unlike th…
Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix Estimation
Ruiyuan Wu, Wing-Kin Ma, Xiao Fu +1
Hyperspectral super-resolution (HSR) is a problem that aims to estimate an image of high spectral and spatial resolutions from a pair of co-registered multispectral (MS) and hypers…