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ORCIDQing Qu 0001
Person information
- affiliation: University of Michigan, Department of Electrical Engineering and Computer Science, Ann Arbor, MI, USA
- affiliation: New York University, Center for Data Science, NY, USA
- affiliation (PhD 2018): Columbia University, Data Science Institute, New York, NY, USA
- affiliation (2016): Microsoft Research, USA
- affiliation (2012 - 2013): United States Army Research Laboratory, MD, USA
- affiliation (former): Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, MD, USA
- affiliation (former): Tsinghua University, Department of Electrical and Computer Engineering, Beijing, China
Other persons with the same name
- Qing Qu — disambiguation page
- Qing Qu 0002
![[0000-0001-7475-7063]](https://dblp.org/img/orcid-mark.12x12.png "0000-0001-7475-7063")
— Yunnan University, School of Chemical Science and Technology, Kunming, China - Qing Qu 0003 — Hong Kong University of Science and Technology, William Mong Institute of Nano Science and Technology, Hong Kong
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2020 – today
- 2025
[i49]Alec S. Xu, Can Yaras, Peng Wang, Qing Qu:
Understanding How Nonlinear Layers Create Linearly Separable Features for Low-Dimensional Data. CoRR abs/2501.02364 (2025)- 2024
- [j11]Xiao Li, Sheng Liu, Jinxin Zhou, Xinyu Lu, Carlos Fernandez-Granda, Zhihui Zhu, Qing Qu:
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse. Trans. Mach. Learn. Res. 2024 (2024) - [c36]Soo Min Kwon, Zekai Zhang, Dogyoon Song, Laura Balzano, Qing Qu:
Efficient Low-Dimensional Compression of Overparameterized Models. AISTATS 2024: 1009-1017 - [c35]Huijie Zhang, Yifu Lu, Ismail Alkhouri, Saiprasad Ravishankar, Dogyoon Song, Qing Qu:
Improving Training Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architecture. CVPR 2024: 7372-7381 - [c34]Ismail Alkhouri, Shijun Liang, Rongrong Wang, Qing Qu, Saiprasad Ravishankar:
Diffusion-Based Adversarial Purification for Robust Deep Mri Reconstruction. ICASSP 2024: 12841-12845 - [c33]Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, Liyue Shen:
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency. ICLR 2024 - [c32]Peng Wang, Huikang Liu, Druv Pai, Yaodong Yu, Zhihui Zhu, Qing Qu, Yi Ma:
A Global Geometric Analysis of Maximal Coding Rate Reduction. ICML 2024 - [c31]Avrajit Ghosh, Xitong Zhang, Kenneth K. Sun, Qing Qu, Saiprasad Ravishankar, Rongrong Wang:
Optimal Eye Surgeon: Finding image priors through sparse generators at initialization. ICML 2024 - [c30]Jiachen Jiang, Jinxin Zhou, Peng Wang, Qing Qu, Dustin G. Mixon, Chong You, Zhihui Zhu:
Generalized Neural Collapse for a Large Number of Classes. ICML 2024 - [c29]Pengyu Li, Xiao Li, Yutong Wang, Qing Qu:
Neural Collapse in Multi-label Learning with Pick-all-label Loss. ICML 2024 - [c28]Huikang Liu, Peng Wang, Longxiu Huang, Qing Qu, Laura Balzano:
Symmetric Matrix Completion with ReLU Sampling. ICML 2024 - [c27]Can Yaras, Peng Wang, Laura Balzano, Qing Qu:
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation. ICML 2024 - [c26]Huijie Zhang, Jinfan Zhou, Yifu Lu, Minzhe Guo, Peng Wang, Liyue Shen, Qing Qu:
The Emergence of Reproducibility and Consistency in Diffusion Models. ICML 2024 - [c25]Changwoo Lee, Soo Min Kwon, Qing Qu, Hun-Seok Kim:
BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference. NeurIPS 2024 - [c24]Ismail Alkhouri, Shijun Liang, Evan Bell, Qing Qu, Rongrong Wang, Saiprasad Ravishankar:
Image Reconstruction Via Autoencoding Sequential Deep Image Prior. NeurIPS 2024 - [c23]Siyi Chen, Huijie Zhang, Minzhe Guo, Yifu Lu, Peng Wang, Qing Qu:
Exploring Low-Dimensional Subspace in Diffusion Models for Controllable Image Editing. NeurIPS 2024 - [c22]Xiang Li, Yixiang Dai, Qing Qu:
Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure. NeurIPS 2024 - [i48]Shijun Liang, Evan Bell, Qing Qu, Rongrong Wang, Saiprasad Ravishankar:
Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction. CoRR abs/2402.04097 (2024) - [i47]Xiang Li, Soo Min Kwon, Ismail R. Alkhouri, Saiprasad Ravishankar, Qing Qu:
Decoupled Data Consistency with Diffusion Purification for Image Restoration. CoRR abs/2403.06054 (2024) - [i46]Jiyi Chen, Pengyu Li, Yutong Wang, Pei-Cheng Ku, Qing Qu:
Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation. CoRR abs/2403.12354 (2024) - [i45]Peng Wang, Huikang Liu, Druv Pai, Yaodong Yu, Zhihui Zhu, Qing Qu, Yi Ma:
A Global Geometric Analysis of Maximal Coding Rate Reduction. CoRR abs/2406.01909 (2024) - [i44]Can Yaras, Peng Wang, Laura Balzano, Qing Qu:
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation. CoRR abs/2406.04112 (2024) - [i43]Avrajit Ghosh, Xitong Zhang, Kenneth K. Sun, Qing Qu, Saiprasad Ravishankar, Rongrong Wang:
Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at Initialization. CoRR abs/2406.05288 (2024) - [i42]Huikang Liu, Peng Wang, Longxiu Huang, Qing Qu, Laura Balzano:
Symmetric Matrix Completion with ReLU Sampling. CoRR abs/2406.05822 (2024) - [i41]Siyi Chen, Minkyu Choi, Zesen Zhao, Kuan Han, Qing Qu, Zhongming Liu:
Unfolding Videos Dynamics via Taylor Expansion. CoRR abs/2409.02371 (2024) - [i40]Siyi Chen, Huijie Zhang, Minzhe Guo, Yifu Lu, Peng Wang, Qing Qu:
Exploring Low-Dimensional Subspaces in Diffusion Models for Controllable Image Editing. CoRR abs/2409.02374 (2024) - [i39]Peng Wang, Huijie Zhang, Zekai Zhang, Siyi Chen, Yi Ma, Qing Qu:
Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering. CoRR abs/2409.02426 (2024) - [i38]Ismail Alkhouri, Shijun Liang, Cheng-Han Huang, Jimmy Dai, Qing Qu, Saiprasad Ravishankar, Rongrong Wang:
SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problems. CoRR abs/2410.04479 (2024) - [i37]Wenda Li, Huijie Zhang, Qing Qu:
Shallow Diffuse: Robust and Invisible Watermarking through Low-Dimensional Subspaces in Diffusion Models. CoRR abs/2410.21088 (2024) - [i36]Changwoo Lee, Soo Min Kwon, Qing Qu, Hun-Seok Kim:
BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference. CoRR abs/2410.21262 (2024) - [i35]Xiang Li, Yixiang Dai, Qing Qu:
Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure. CoRR abs/2410.24060 (2024) - [i34]Can Yaras, Siyi Chen, Peng Wang, Qing Qu:
Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning. CoRR abs/2412.07909 (2024) - [i33]Huminhao Zhu, Fangyikang Wang, Tianyu Ding, Qing Qu, Zhihui Zhu:
Analyzing and Improving Model Collapse in Rectified Flow Models. CoRR abs/2412.08175 (2024) - 2023
- [c21]Xiang Li, Saiprasad Ravishankar, Qing Qu:
Robust Deep Image Recovery from Sparsely Corrupted and Sub-Sampled Measurements. CAMSAP 2023: 521-525 - [c20]Evan Bell, Shijun Liang, Qing Qu, Saiprasad Ravishankar:
Robust Self-Guided Deep Image Prior. ICASSP 2023: 1-5 - [i32]Can Yaras, Peng Wang, Wei Hu, Zhihui Zhu, Laura Balzano, Qing Qu:
The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks. CoRR abs/2306.01154 (2023) - [i31]Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, Liyue Shen:
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency. CoRR abs/2307.08123 (2023) - [i30]Yuexiang Zhai, Shengbang Tong, Xiao Li, Mu Cai, Qing Qu, Yong Jae Lee, Yi Ma:
Investigating the Catastrophic Forgetting in Multimodal Large Language Models. CoRR abs/2309.10313 (2023) - [i29]Huijie Zhang, Jinfan Zhou, Yifu Lu, Minzhe Guo, Liyue Shen, Qing Qu:
The Emergence of Reproducibility and Consistency in Diffusion Models. CoRR abs/2310.05264 (2023) - [i28]Jiachen Jiang, Jinxin Zhou, Peng Wang, Qing Qu, Dustin G. Mixon, Chong You, Zhihui Zhu:
Generalized Neural Collapse for a Large Number of Classes. CoRR abs/2310.05351 (2023) - [i27]Pengyu Li, Yutong Wang, Xiao Li, Qing Qu:
Neural Collapse in Multi-label Learning with Pick-all-label Loss. CoRR abs/2310.15903 (2023) - [i26]Peng Wang, Xiao Li, Can Yaras, Zhihui Zhu, Laura Balzano, Wei Hu, Qing Qu:
Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination. CoRR abs/2311.02960 (2023) - [i25]Soo Min Kwon, Zekai Zhang, Dogyoon Song, Laura Balzano, Qing Qu:
Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics. CoRR abs/2311.05061 (2023) - [i24]Huijie Zhang, Yifu Lu, Ismail Alkhouri, Saiprasad Ravishankar, Dogyoon Song, Qing Qu:
Improving Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures. CoRR abs/2312.09181 (2023) - 2022
- [c19]Sheng Liu, Zhihui Zhu, Qing Qu, Chong You:
Robust Training under Label Noise by Over-parameterization. ICML 2022: 14153-14172 - [c18]Jinxin Zhou, Xiao Li, Tianyu Ding, Chong You, Qing Qu, Zhihui Zhu:
On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features. ICML 2022: 27179-27202 - [c17]Can Yaras, Peng Wang, Zhihui Zhu, Laura Balzano, Qing Qu:
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold. NeurIPS 2022 - [c16]Jinxin Zhou, Chong You, Xiao Li, Kangning Liu, Sheng Liu, Qing Qu, Zhihui Zhu:
Are All Losses Created Equal: A Neural Collapse Perspective. NeurIPS 2022 - [i23]Sheng Liu, Zhihui Zhu, Qing Qu, Chong You:
Robust Training under Label Noise by Over-parameterization. CoRR abs/2202.14026 (2022) - [i22]Jinxin Zhou, Xiao Li, Tianyu Ding, Chong You, Qing Qu, Zhihui Zhu:
On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features. CoRR abs/2203.01238 (2022) - [i21]Can Yaras, Peng Wang, Zhihui Zhu, Laura Balzano, Qing Qu:
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold. CoRR abs/2209.09211 (2022) - [i20]Jinxin Zhou, Chong You, Xiao Li, Kangning Liu, Sheng Liu, Qing Qu, Zhihui Zhu:
Are All Losses Created Equal: A Neural Collapse Perspective. CoRR abs/2210.02192 (2022) - [i19]Xiao Li, Sheng Liu, Jinxin Zhou, Xinyu Lu, Carlos Fernandez-Granda, Zhihui Zhu, Qing Qu:
Principled and Efficient Transfer Learning of Deep Models via Neural Collapse. CoRR abs/2212.12206 (2022) - 2021
- [j10]Xiao Li, Shixiang Chen, Zengde Deng, Qing Qu, Zhihui Zhu, Anthony Man-Cho So:
Weakly Convex Optimization over Stiefel Manifold Using Riemannian Subgradient-Type Methods. SIAM J. Optim. 31(3): 1605-1634 (2021) - [c15]Lijun Ding, Liwei Jiang, Yudong Chen, Qing Qu, Zhihui Zhu:
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery. NeurIPS 2021: 26767-26778 - [c14]Sheng Liu, Xiao Li, Yuexiang Zhai, Chong You, Zhihui Zhu, Carlos Fernandez-Granda, Qing Qu:
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training. NeurIPS 2021: 28919-28928 - [c13]Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu:
A Geometric Analysis of Neural Collapse with Unconstrained Features. NeurIPS 2021: 29820-29834 - [i18]Sheng Liu, Xiao Li, Yuexiang Zhai, Chong You, Zhihui Zhu, Carlos Fernandez-Granda, Qing Qu:
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training. CoRR abs/2103.00673 (2021) - [i17]Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu:
A Geometric Analysis of Neural Collapse with Unconstrained Features. CoRR abs/2105.02375 (2021) - [i16]Lijun Ding, Liwei Jiang, Yudong Chen, Qing Qu, Zhihui Zhu:
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery. CoRR abs/2109.11154 (2021) - 2020
- [j9]Qing Qu, Xiao Li, Zhihui Zhu:
Exact Recovery of Multichannel Sparse Blind Deconvolution via Gradient Descent. SIAM J. Imaging Sci. 13(3): 1630-1652 (2020) - [j8]Qing Qu, Yuqian Zhang, Yonina C. Eldar, John Wright:
Convolutional Phase Retrieval via Gradient Descent. IEEE Trans. Inf. Theory 66(3): 1785-1821 (2020) - [c12]Yenson Lau, Qing Qu, Han-Wen Kuo, Pengcheng Zhou, Yuqian Zhang, John Wright:
Short and Sparse Deconvolution - A Geometric Approach. ICLR 2020 - [c11]Qing Qu, Yuexiang Zhai, Xiao Li, Yuqian Zhang, Zhihui Zhu:
Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning. ICLR 2020 - [c10]Chong You, Zhihui Zhu, Qing Qu, Yi Ma:
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization. NeurIPS 2020 - [i15]Qing Qu, Zhihui Zhu, Xiao Li, Manolis C. Tsakiris, John Wright, René Vidal:
Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications. CoRR abs/2001.06970 (2020) - [i14]Chong You, Zhihui Zhu, Qing Qu, Yi Ma:
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization. CoRR abs/2006.08857 (2020) - [i13]Yuqian Zhang, Qing Qu, John Wright:
From Symmetry to Geometry: Tractable Nonconvex Problems. CoRR abs/2007.06753 (2020)
2010 – 2019
- 2019
- [c9]Qing Qu, Xiao Li, Zhihui Zhu:
Exact and Efficient Multi-Channel Sparse Blind Deconvolution - A Nonconvex Approach. ACSSC 2019: 640-644 - [c8]Qing Qu, Xiao Li, Zhihui Zhu:
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution. NeurIPS 2019: 4017-4028 - [i12]Qing Qu, Xiao Li, Zhihui Zhu:
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution. CoRR abs/1908.10776 (2019) - [i11]Yenson Lau, Qing Qu, Han-Wen Kuo, Pengcheng Zhou, Yuqian Zhang, John Wright:
Short-and-Sparse Deconvolution - A Geometric Approach. CoRR abs/1908.10959 (2019) - [i10]Xiao Li, Shixiang Chen, Zengde Deng, Qing Qu, Zhihui Zhu, Anthony Man-Cho So:
Nonsmooth Optimization over Stiefel Manifold: Riemannian Subgradient Methods. CoRR abs/1911.05047 (2019) - [i9]Qing Qu, Yuexiang Zhai, Xiao Li, Yuqian Zhang, Zhihui Zhu:
Analysis of the Optimization Landscapes for Overcomplete Representation Learning. CoRR abs/1912.02427 (2019) - 2018
- [j7]Ju Sun, Qing Qu, John Wright:
A Geometric Analysis of Phase Retrieval. Found. Comput. Math. 18(5): 1131-1198 (2018) - 2017
- [j6]Ju Sun, Qing Qu, John Wright:
Complete Dictionary Recovery Over the Sphere I: Overview and the Geometric Picture. IEEE Trans. Inf. Theory 63(2): 853-884 (2017) - [j5]Ju Sun, Qing Qu, John Wright:
Complete Dictionary Recovery Over the Sphere II: Recovery by Riemannian Trust-Region Method. IEEE Trans. Inf. Theory 63(2): 885-914 (2017) - [c7]Qing Qu, Yuqian Zhang, Yonina C. Eldar, John Wright:
Convolutional Phase Retrieval. NIPS 2017: 6086-6096 - [i8]Qing Qu, Yuqian Zhang, Yonina C. Eldar, John Wright:
Convolutional Phase Retrieval via Gradient Descent. CoRR abs/1712.00716 (2017) - 2016
- [j4]Qing Qu, Ju Sun, John Wright:
Finding a Sparse Vector in a Subspace: Linear Sparsity Using Alternating Directions. IEEE Trans. Inf. Theory 62(10): 5855-5880 (2016) - [c6]Ju Sun, Qing Qu, John Wright:
A geometric analysis of phase retrieval. ISIT 2016: 2379-2383 - [i7]Ju Sun, Qing Qu, John Wright:
A Geometric Analysis of Phase Retrieval. CoRR abs/1602.06664 (2016) - 2015
- [j3]Qing Qu, Nasser M. Nasrabadi, Trac D. Tran:
Subspace Vertex Pursuit: A Fast and Robust Near-Separable Nonnegative Matrix Factorization Method for Hyperspectral Unmixing. IEEE J. Sel. Top. Signal Process. 9(6): 1142-1155 (2015) - [c5]Ju Sun, Qing Qu, John Wright:
Complete Dictionary Recovery Using Nonconvex Optimization. ICML 2015: 2351-2360 - [i6]Ju Sun, Qing Qu, John Wright:
Complete Dictionary Recovery over the Sphere. CoRR abs/1504.06785 (2015) - [i5]Ju Sun, Qing Qu, John Wright:
When Are Nonconvex Problems Not Scary? CoRR abs/1510.06096 (2015) - [i4]Ju Sun, Qing Qu, John Wright:
Complete Dictionary Recovery over the Sphere I: Overview and the Geometric Picture. CoRR abs/1511.03607 (2015) - [i3]Ju Sun, Qing Qu, John Wright:
Complete Dictionary Recovery over the Sphere II: Recovery by Riemannian Trust-region Method. CoRR abs/1511.04777 (2015) - 2014
- [j2]Xiaoxia Sun, Qing Qu, Nasser M. Nasrabadi, Trac D. Tran:
Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification. IEEE Geosci. Remote. Sens. Lett. 11(7): 1235-1239 (2014) - [j1]Qing Qu, Nasser M. Nasrabadi, Trac D. Tran:
Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation. IEEE Trans. Geosci. Remote. Sens. 52(7): 4404-4423 (2014) - [c4]Qing Qu, Xiaoxia Sun, Nasser M. Nasrabadi, Trac D. Tran:
Subspace vertex pursuit for separable non-negative matrix factorization in hyperspectral unmixing. ICASSP 2014: 8115-8119 - [c3]Qing Qu, Ju Sun, John Wright:
Finding a sparse vector in a subspace: Linear sparsity using alternating directions. NIPS 2014: 3401-3409 - [i2]Xiaoxia Sun, Qing Qu, Nasser M. Nasrabadi, Trac D. Tran:
Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification. CoRR abs/1401.3818 (2014) - [i1]Qing Qu, Ju Sun, John Wright:
Finding a sparse vector in a subspace: Linear sparsity using alternating directions. CoRR abs/1412.4659 (2014) - 2013
- [c2]Qing Qu, Nasser M. Nasrabadi, Trac D. Tran:
Hyperspectral abundance estimation for the generalized bilinear model with joint sparsity constraint. ICASSP 2013: 2129-2133 - [c1]Qing Qu, Xiaoxia Sun, Nasser M. Nasrabadi, Trac D. Tran:
Low rank representation for bilinear abundance estimation problem. WHISPERS 2013: 1-4
Coauthor Index
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