11 citations · 60 across the 15 of their papers we have counts for
9 papers · 1 filter
Misspecified Phase Retrieval with Generative Priors
Zhaoqiang Liu, Xinshao Wang, Jiulong Liu
In this paper, we study phase retrieval under model misspecification and generative priors. In particular, we aim to estimate an -dimensional signal from i.i.d.…
Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors
Zhaoqiang Liu, Jun Han
In this paper, we propose projected gradient descent (PGD) algorithms for signal estimation from noisy nonlinear measurements. We assume that the unknown -dimensional signal lie…
Generative Principal Component Analysis
Zhaoqiang Liu, Jiulong Liu, Subhroshekhar Ghosh +2
In this paper, we study the problem of principal component analysis with generative modeling assumptions, adopting a general model for the observed matrix that encompasses notable…
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors
Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett
Compressive phase retrieval is a popular variant of the standard compressive sensing problem in which the measurements only contain magnitude information. In this paper, motivated…
The Generalized Lasso with Nonlinear Observations and Generative Priors
Zhaoqiang Liu, Jonathan Scarlett
In this paper, we study the problem of signal estimation from noisy non-linear measurements when the unknown -dimensional signal is in the range of an -Lipschitz continuous g…
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari +1
The goal of standard 1-bit compressive sensing is to accurately recover an unknown sparse vector from binary-valued measurements, each indicating the sign of a linear function of t…
Model Selection for Nonnegative Matrix Factorization by Support Union Recovery
Zhaoqiang Liu
Nonnegative matrix factorization (NMF) has been widely used in machine learning and signal processing because of its non-subtractive, part-based property which enhances interpretab…
The Informativeness of K -Means for Learning Mixture Models
Zhaoqiang Liu, Vincent Y. F. Tan
The learning of mixture models can be viewed as a clustering problem. Indeed, given data samples independently generated from a mixture of distributions, we often would like to fin…
Rank-One NMF-Based Initialization for NMF and Relative Error Bounds under a Geometric Assumption
Zhaoqiang Liu, Vincent Y. F. Tan
We propose a geometric assumption on nonnegative data matrices such that under this assumption, we are able to provide upper bounds (both deterministic and probabilistic) on the re…