20 citations · 58 across the 11 of their papers we have counts for
14 papers
Near-Optimal Nonconvex Matrix Completion
Jian-Feng Cai, Xiliang Lu, Juntao You
We study nonconvex methods for matrix completion, the problem of recovering a low-rank matrix from a subset of its entries. Convex methods achieve sample complexity linear in the m…
Off-Grid Point-Scatterer Localization from Sparse Limited-Aperture Data via Hankel Completion
Jingzhi Li, Xiliang Lu, Juntao You
We study the localization of distinct planar point scatterers from sparsely sampled single-incidence, single-frequency acoustic far-field data over a limited receiver aperture.…
Quantized Low-Rank Quantum State Tomography: Hyperbolic Quantization and Riemannian Least-Squares Recovery
HanQin Cai, Longxiu Huang, Juntao You
We study low-rank quantum state tomography from finite-bit Pauli batch responses. To avoid bias introduced by generic quantization, we propose HyperQuant, a mean-preserving hyperbo…
Robust Spectral Recovery for Dynamical Sampling
HanQin Cai, Longxiu Huang, Tianming Wang +1
We study the spectral recovery problem for dynamical sampling on a finite cyclic grid. Given time snapshots obtained from a fixed uniform spatial subsampling of the orbit $x_{\ell}…
Online Quantum State Tomography via Stochastic Gradient Descent
Jian-Feng Cai, Yuling Jiao, Yinan Li +3
We initiate the study of online quantum state tomography (QST), where the matrix representation of an unknown quantum state is reconstructed by sequentially performing a batch of m…
GraHTP: A Provable Newton-like Algorithm for Sparse Phase Retrieval
Licheng Dai, Xiliang Lu, Juntao You
This paper investigates the sparse phase retrieval problem, which aims to recover a sparse signal from a system of quadratic measurements. In this work, we propose a novel non-conv…
Guaranteed Sampling Flexibility for Low-tubal-rank Tensor Completion
Bowen Su, Juntao You, HanQin Cai +1
While Bernoulli sampling is extensively studied in tensor completion, t-CUR sampling approximates low-tubal-rank tensors via lateral and horizontal subtensors. However, both method…
Accelerating Ill-conditioned Hankel Matrix Recovery via Structured Newton-like Descent
HanQin Cai, Longxiu Huang, Xiliang Lu +1
This paper studies the robust Hankel recovery problem, which simultaneously removes the sparse outliers and fulfills missing entries from the partial observation. We propose a nove…
Provable Sample-Efficient Sparse Phase Retrieval Initialized by Truncated Power Method
Jian-Feng Cai, Jingyang Li, Juntao You
We study the sparse phase retrieval problem, recovering an -sparse length- signal from magnitude-only measurements. Two-stage non-convex approaches have drawn much attent…
Structured Gradient Descent for Fast Robust Low-Rank Hankel Matrix Completion
HanQin Cai, Jian-Feng Cai, Juntao You
We study the robust matrix completion problem for the low-rank Hankel matrix, which detects the sparse corruptions caused by extreme outliers while we try to recover the original H…
An Efficient Two-Stage SPARC Decoder for Massive MIMO Unsourced Random Access
Juntao You, Wenjie Wang, Shansuo Liang +2
In this paper, we study a concatenate coding scheme based on sparse regression code (SPARC) and tree code for unsourced random access in massive multiple-input and multiple-output…
Sample-Efficient Sparse Phase Retrieval via Stochastic Alternating Minimization
Jian-Feng Cai, Yuling Jiao, Xiliang Lu +1
In this work we propose a nonconvex two-stage \underline{s}tochastic \underline{a}lternating \underline{m}inimizing (SAM) method for sparse phase retrieval. The proposed algorithm…
Sparse Signal Recovery from Phaseless Measurements via Hard Thresholding Pursuit
Jian-Feng Cai, Jingzhi Li, Xiliang Lu +1
In this paper, we consider the sparse phase retrieval problem, recovering an -sparse signal from phaseless samples $y_i=|\langle\bm{x}^{\n…
A stochastic alternating minimizing method for sparse phase retrieval
Jianfeng Cai, Yuling Jiao, Xiliang Lu +1
Sparse phase retrieval plays an important role in many fields of applied science and thus attracts lots of attention. In this paper, we propose a \underline{sto}chastic alte\underl…