Relative error bounds for nonnegative matrix factorization under a geometric assumption. - Zhaoqiang Liu; Vincent Y. F. Tan (ICASSP, 2017)

The Informativeness of k-Means for Learning Gaussian Mixture Models. - Zhaoqiang Liu; Vincent Y. F. Tan (CoRR, 2017)

Rank-One NMF-Based Initialization for NMF and Relative Error Bounds Under a Geometric Assumption. - Zhaoqiang Liu; Vincent Y. F. Tan (IEEE Trans. Signal Process., 2017)

Minimax Lower Bounds for Nonnegative Matrix Factorization. - Mine Alsan; Zhaoqiang Liu; Vincent Y. F. Tan (SSP, 2018)

Model Selection for Nonnegative Matrix Factorization by Support Union Recovery. - Zhaoqiang Liu (ICASSP, 2019)

Error Bounds for Spectral Clustering over Samples from Spherical Gaussian Mixture Models. - Zhaoqiang Liu (ICASSP, 2019)

The Informativeness of k-Means for Learning Mixture Models. - Zhaoqiang Liu; Vincent Y. F. Tan (IEEE Trans. Inf. Theory, 2019)

Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors. - Zhaoqiang Liu; Selwyn Gomes; Avtansh Tiwari; Jonathan Scarlett (ICML, 2020)

The Generalized Lasso with Nonlinear Observations and Generative Priors. - Zhaoqiang Liu; Jonathan Scarlett (NeurIPS, 2020)

Sample Complexity Lower Bounds for Compressive Sensing with Generative Models. - Zhaoqiang Liu; Jonathan Scarlett (SPCOM, 2020)

Information-Theoretic Lower Bounds for Compressive Sensing With Generative Models. - Zhaoqiang Liu; Jonathan Scarlett (IEEE J. Sel. Areas Inf. Theory, 2020)

Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors. - Zhaoqiang Liu; Subhroshekhar Ghosh; Jonathan Scarlett (ITW, 2021)

Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors. - Zhaoqiang Liu; Subhroshekhar Ghosh; Jonathan Scarlett (NeurIPS, 2021)

Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors. - Zhaoqiang Liu; Subhroshekhar Ghosh; Jun Han; Jonathan Scarlett (CoRR, 2021)

Non-Iterative Recovery from Nonlinear Observations using Generative Models. - Jiulong Liu; Zhaoqiang Liu (CVPR, 2022)

Generative Principal Component Analysis. - Zhaoqiang Liu; Jiulong Liu; Subhroshekhar Ghosh; Jun Han; Jonathan Scarlett (ICLR, 2022)

Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors. - Zhaoqiang Liu; Jun Han (IJCAI, 2022)

Misspecified Phase Retrieval with Generative Priors. - Zhaoqiang Liu; Xinshao Wang; Jiulong Liu (NeurIPS, 2022)

DDP: Diffusion Model for Dense Visual Prediction. - Yuanfeng Ji; Zhe Chen 017; Enze Xie; Lanqing Hong; Xihui Liu; Zhaoqiang Liu; Tong Lu; Zhenguo Li; Ping Luo 002 (ICCV, 2023)

DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning. - Enze Xie; Lewei Yao; Han Shi; Zhili Liu; Daquan Zhou; Zhaoqiang Liu; Jiawei Li; Zhenguo Li (ICCV, 2023)

A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing. - Junren Chen; Jonathan Scarlett; Michael Ng 001; Zhaoqiang Liu (NeurIPS, 2023)

Solving Quadratic Systems with Full-Rank Matrices Using Sparse or Generative Priors. - Junren Chen; Shuai Huang; Michael K. Ng 001; Zhaoqiang Liu (CoRR, 2023)

Spatial-temporal characteristics of carbon emissions corrected by socio-economic driving factors under land use changes in Sichuan Province, southwestern China. - Can Cai; Min Fan; Jing Yao; Lele Zhou; Yuanzhe Wang; Xiaoying Liang; Zhaoqiang Liu; Shu Chen (Ecol. Informatics, 2023)

Efficient Algorithms for Non-gaussian Single Index Models with Generative Priors. - Junren Chen; Zhaoqiang Liu (AAAI, 2024)

Accelerating Diffusion Sampling with Optimized Time Steps. - Shuchen Xue; Zhaoqiang Liu; Fei Chen; Shifeng Zhang; Tianyang Hu; Enze Xie; Zhenguo Li (CVPR, 2024)

The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling. - Jiajun Ma; Shuchen Xue; Tianyang Hu; Wenjia Wang; Zhaoqiang Liu; Zhenguo Li; Zhi-Ming Ma; Kenji Kawaguchi (ICML, 2024)

Generalized Eigenvalue Problems with Generative Priors. - Zhaoqiang Liu; Wen Li; Junren Chen (NeurIPS, 2024)

On the Expressive Power of a Variant of the Looped Transformer. - Yihang Gao; Chuanyang Zheng; Enze Xie; Han Shi; Tianyang Hu; Yu Li; Michael K. Ng 001; Zhenguo Li; Zhaoqiang Liu (CoRR, 2024)

Robust Instance Optimal Phase-Only Compressed Sensing. - Junren Chen; Zhaoqiang Liu; Michael Kwok-Po Ng; Jonathan Scarlett (CoRR, 2024)

An Affine Equivalence Algorithm for S-boxes based on Matrix Invariants. - Xincheng Hu; Xiao Zeng; Zhaoqiang Liu; Guowu Yang (CoRR, 2024)

Uniform Recovery Guarantees for Quantized Corrupted Sensing Using Structured or Generative Priors. - Junren Chen; Zhaoqiang Liu; Meng Ding 002; Michael K. Ng 001 (SIAM J. Imaging Sci., 2024)

AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures. - Yihang Gao; Chuanyang Zheng; Enze Xie; Han Shi; Tianyang Hu; Yu Li; Michael Ng 001; Zhenguo Li; Zhaoqiang Liu (Trans. Mach. Learn. Res., 2025)

Solving Quadratic Systems With Full-Rank Matrices Using Sparse or Generative Priors. - Junren Chen; Michael Kwok-Po Ng; Zhaoqiang Liu (IEEE Trans. Signal Process., 2025)

An Expectation-Maximization Algorithm-based Autoregressive Model for the Fuzzy Job Shop Scheduling Problem. - Yijian Wang; Tongxian Guo; Zhaoqiang Liu (CoRR, 2025)

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models. - Yang Zheng; Wen Li; Zhaoqiang Liu (CoRR, 2025)

Learning Single Index Models with Diffusion Priors. - Anqi Tang; Youming Chen; Shuchen Xue; Zhaoqiang Liu (CoRR, 2025)