Hyperspectral abundance estimation for the generalized bilinear model with joint sparsity constraint. - Qing Qu 001; Nasser M. Nasrabadi; Trac D. Tran (ICASSP, 2013)

Low rank representation for bilinear abundance estimation problem. - Qing Qu 001; Xiaoxia Sun; Nasser M. Nasrabadi; Trac D. Tran (WHISPERS, 2013)

Subspace vertex pursuit for separable non-negative matrix factorization in hyperspectral unmixing. - Qing Qu 001; Xiaoxia Sun; Nasser M. Nasrabadi; Trac D. Tran (ICASSP, 2014)

Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification. - Xiaoxia Sun; Qing Qu 001; Nasser M. Nasrabadi; Trac D. Tran (IEEE Geosci. Remote. Sens. Lett., 2014)

Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation. - Qing Qu 001; Nasser M. Nasrabadi; Trac D. Tran (IEEE Trans. Geosci. Remote. Sens., 2014)

Complete Dictionary Recovery Using Nonconvex Optimization. - Ju Sun; Qing Qu 001; John Wright 001 (ICML, 2015)

Complete Dictionary Recovery over the Sphere. - Ju Sun; Qing Qu 001; John Wright 001 (CoRR, 2015)

When Are Nonconvex Problems Not Scary? - Ju Sun; Qing Qu 001; John Wright 001 (CoRR, 2015)

Subspace Vertex Pursuit: A Fast and Robust Near-Separable Nonnegative Matrix Factorization Method for Hyperspectral Unmixing. - Qing Qu 001; Nasser M. Nasrabadi; Trac D. Tran (IEEE J. Sel. Top. Signal Process., 2015)

Finding a Sparse Vector in a Subspace: Linear Sparsity Using Alternating Directions. - Qing Qu 001; Ju Sun; John Wright 001 (IEEE Trans. Inf. Theory, 2016)

Convolutional Phase Retrieval. - Qing Qu 001; Yuqian Zhang; Yonina C. Eldar; John Wright 001 (NIPS, 2017)

Complete Dictionary Recovery Over the Sphere I: Overview and the Geometric Picture. - Ju Sun; Qing Qu 001; John Wright 001 (IEEE Trans. Inf. Theory, 2017)

Complete Dictionary Recovery Over the Sphere II: Recovery by Riemannian Trust-Region Method. - Ju Sun; Qing Qu 001; John Wright 001 (IEEE Trans. Inf. Theory, 2017)

A Geometric Analysis of Phase Retrieval. - Ju Sun; Qing Qu 001; John Wright 001 (Found. Comput. Math., 2018)

Exact and Efficient Multi-Channel Sparse Blind Deconvolution - A Nonconvex Approach. - Qing Qu 001; Xiao Li 009; Zhihui Zhu (ACSSC, 2019)

A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution. - Qing Qu 001; Xiao Li 009; Zhihui Zhu (NeurIPS, 2019)

Nonsmooth Optimization over Stiefel Manifold: Riemannian Subgradient Methods. - Xiao Li 009; Shixiang Chen; Zengde Deng; Qing Qu 001; Zhihui Zhu; Anthony Man-Cho So (CoRR, 2019)

Analysis of the Optimization Landscapes for Overcomplete Representation Learning. - Qing Qu 001; Yuexiang Zhai; Xiao Li 009; Yuqian Zhang; Zhihui Zhu (CoRR, 2019)

Short and Sparse Deconvolution - A Geometric Approach. - Yenson Lau; Qing Qu 001; Han-Wen Kuo; Pengcheng Zhou; Yuqian Zhang; John Wright 001 (ICLR, 2020)

Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning. - Qing Qu 001; Yuexiang Zhai; Xiao Li 009; Yuqian Zhang; Zhihui Zhu (ICLR, 2020)

Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization. - Chong You; Zhihui Zhu; Qing Qu 001; Yi Ma 001 (NeurIPS, 2020)

Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications. - Qing Qu 001; Zhihui Zhu; Xiao Li 009; Manolis C. Tsakiris; John Wright 001; René Vidal (CoRR, 2020)

From Symmetry to Geometry: Tractable Nonconvex Problems. - Yuqian Zhang; Qing Qu 001; John Wright 001 (CoRR, 2020)

Exact Recovery of Multichannel Sparse Blind Deconvolution via Gradient Descent. - Qing Qu 001; Xiao Li 009; Zhihui Zhu (SIAM J. Imaging Sci., 2020)

Convolutional Phase Retrieval via Gradient Descent. - Qing Qu 001; Yuqian Zhang; Yonina C. Eldar; John Wright 001 (IEEE Trans. Inf. Theory, 2020)

Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery. - Lijun Ding; Liwei Jiang; Yudong Chen 001; Qing Qu 001; Zhihui Zhu (NeurIPS, 2021)

Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training. - Sheng Liu; Xiao Li 026; Yuexiang Zhai; Chong You; Zhihui Zhu; Carlos Fernandez-Granda; Qing Qu 001 (NeurIPS, 2021)

A Geometric Analysis of Neural Collapse with Unconstrained Features. - Zhihui Zhu; Tianyu Ding; Jinxin Zhou; Xiao Li 026; Chong You; Jeremias Sulam; Qing Qu 001 (NeurIPS, 2021)

Weakly Convex Optimization over Stiefel Manifold Using Riemannian Subgradient-Type Methods. - Xiao Li 009; Shixiang Chen; Zengde Deng; Qing Qu 001; Zhihui Zhu; Anthony Man-Cho So (SIAM J. Optim., 2021)

Robust Training under Label Noise by Over-parameterization. - Sheng Liu; Zhihui Zhu; Qing Qu 001; Chong You (ICML, 2022)

On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features. - Jinxin Zhou; Xiao Li 026; Tianyu Ding; Chong You; Qing Qu 001; Zhihui Zhu (ICML, 2022)

Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold. - Can Yaras; Peng Wang 098; Zhihui Zhu; Laura Balzano; Qing Qu 001 (NeurIPS, 2022)

Are All Losses Created Equal: A Neural Collapse Perspective. - Jinxin Zhou; Chong You; Xiao Li 026; Kangning Liu; Sheng Liu; Qing Qu 001; Zhihui Zhu (NeurIPS, 2022)

Principled and Efficient Transfer Learning of Deep Models via Neural Collapse. - Xiao Li 026; Sheng Liu; Jinxin Zhou; Xinyu Lu; Carlos Fernandez-Granda; Zhihui Zhu; Qing Qu 001 (CoRR, 2022)

Robust Deep Image Recovery from Sparsely Corrupted and Sub-Sampled Measurements. - Xiang Li; Saiprasad Ravishankar; Qing Qu 001 (CAMSAP, 2023)

Robust Self-Guided Deep Image Prior. - Evan Bell; Shijun Liang; Qing Qu 001; Saiprasad Ravishankar (ICASSP, 2023)

The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks. - Can Yaras; Peng Wang 098; Wei Hu; Zhihui Zhu; Laura Balzano; Qing Qu 001 (CoRR, 2023)

Investigating the Catastrophic Forgetting in Multimodal Large Language Models. - Yuexiang Zhai; Shengbang Tong; Xiao Li 022; Mu Cai; Qing Qu 001; Yong Jae Lee; Yi Ma 001 (CoRR, 2023)

The Emergence of Reproducibility and Consistency in Diffusion Models. - Huijie Zhang; Jinfan Zhou; Yifu Lu; Minzhe Guo; Liyue Shen; Qing Qu 001 (CoRR, 2023)

Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination. - Peng Wang 098; Xiao Li; Can Yaras; Zhihui Zhu; Laura Balzano; Wei Hu; Qing Qu 001 (CoRR, 2023)

Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics. - Soo Min Kwon; Zekai Zhang; Dogyoon Song; Laura Balzano; Qing Qu 001 (CoRR, 2023)

Improving Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures. - Huijie Zhang; Yifu Lu; Ismail Alkhouri; Saiprasad Ravishankar; Dogyoon Song; Qing Qu 001 (CoRR, 2023)

Efficient Low-Dimensional Compression of Overparameterized Models. - Soo Min Kwon; Zekai Zhang; Dogyoon Song; Laura Balzano; Qing Qu 001 (AISTATS, 2024)

Investigating the Catastrophic Forgetting in Multimodal Large Language Model Fine-Tuning. - Yuexiang Zhai; Shengbang Tong; Xiao Li 022; Mu Cai; Qing Qu 001; Yong Jae Lee; Yi Ma 001 (CPAL, 2024)

Improving Training Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architecture. - Huijie Zhang; Yifu Lu; Ismail Alkhouri; Saiprasad Ravishankar; Dogyoon Song; Qing Qu 001 (CVPR, 2024)

Diffusion-Based Adversarial Purification for Robust Deep Mri Reconstruction. - Ismail Alkhouri; Shijun Liang; Rongrong Wang; Qing Qu 001; Saiprasad Ravishankar (ICASSP, 2024)

Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency. - Bowen Song; Soo Min Kwon; Zecheng Zhang; Xinyu Hu; Qing Qu 001; Liyue Shen (ICLR, 2024)

A Global Geometric Analysis of Maximal Coding Rate Reduction. - Peng Wang 098; Huikang Liu; Druv Pai; Yaodong Yu; Zhihui Zhu; Qing Qu 001; Yi Ma 001 (ICML, 2024)

Optimal Eye Surgeon: Finding image priors through sparse generators at initialization. - Avrajit Ghosh; Xitong Zhang; Kenneth K. Sun; Qing Qu 001; Saiprasad Ravishankar; Rongrong Wang (ICML, 2024)

Generalized Neural Collapse for a Large Number of Classes. - Jiachen Jiang; Jinxin Zhou; Peng Wang 098; Qing Qu 001; Dustin G. Mixon; Chong You; Zhihui Zhu (ICML, 2024)

Neural Collapse in Multi-label Learning with Pick-all-label Loss. - Pengyu Li; Xiao Li; Yutong Wang 002; Qing Qu 001 (ICML, 2024)

Symmetric Matrix Completion with ReLU Sampling. - Huikang Liu; Peng Wang 098; Longxiu Huang; Qing Qu 001; Laura Balzano (ICML, 2024)

Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation. - Can Yaras; Peng Wang 098; Laura Balzano; Qing Qu 001 (ICML, 2024)

The Emergence of Reproducibility and Consistency in Diffusion Models. - Huijie Zhang; Jinfan Zhou; Yifu Lu; Minzhe Guo; Peng Wang 098; Liyue Shen; Qing Qu 001 (ICML, 2024)

BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference. - Changwoo Lee 001; Soo Min Kwon; Qing Qu 001; Hun-Seok Kim (NeurIPS, 2024)

Image Reconstruction Via Autoencoding Sequential Deep Image Prior. - Ismail Alkhouri; Shijun Liang; Evan Bell; Qing Qu 001; Rongrong Wang; Saiprasad Ravishankar (NeurIPS, 2024)

Exploring Low-Dimensional Subspace in Diffusion Models for Controllable Image Editing. - Siyi Chen; Huijie Zhang; Minzhe Guo; Yifu Lu; Peng Wang 098; Qing Qu 001 (NeurIPS, 2024)

Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure. - Xiang Li; Yixiang Dai; Qing Qu 001 (NeurIPS, 2024)

Decoupled Data Consistency with Diffusion Purification for Image Restoration. - Xiang Li; Soo Min Kwon; Ismail R. Alkhouri; Saiprasad Ravishankar; Qing Qu 001 (CoRR, 2024)

Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation. - Jiyi Chen; Pengyu Li; Yutong Wang 002; Pei-Cheng Ku; Qing Qu 001 (CoRR, 2024)

Unfolding Videos Dynamics via Taylor Expansion. - Siyi Chen; Minkyu Choi; Zesen Zhao; Kuan Han; Qing Qu 001; Zhongming Liu (CoRR, 2024)

Exploring Low-Dimensional Subspaces in Diffusion Models for Controllable Image Editing. - Siyi Chen; Huijie Zhang; Minzhe Guo; Yifu Lu; Peng Wang 098; Qing Qu 001 (CoRR, 2024)

Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering. - Peng Wang 098; Huijie Zhang; Zekai Zhang; Siyi Chen; Yi Ma 001; Qing Qu 001 (CoRR, 2024)

SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problems. - Ismail Alkhouri; Shijun Liang; Cheng-Han Huang; Jimmy Dai; Qing Qu 001; Saiprasad Ravishankar; Rongrong Wang (CoRR, 2024)

Shallow Diffuse: Robust and Invisible Watermarking through Low-Dimensional Subspaces in Diffusion Models. - Wenda Li 005; Huijie Zhang; Qing Qu 001 (CoRR, 2024)

Analyzing and Improving Model Collapse in Rectified Flow Models. - Huminhao Zhu; Fangyikang Wang; Tianyu Ding; Qing Qu 001; Zhihui Zhu (CoRR, 2024)

Understanding and Improving Transfer Learning of Deep Models via Neural Collapse. - Xiao Li 026; Sheng Liu; Jinxin Zhou; Xinyu Lu; Carlos Fernandez-Granda; Zhihui Zhu; Qing Qu 001 (Trans. Mach. Learn. Res., 2024)

Enhancing Video Representation Learning with Temporal Differentiation. - Siyi Chen; Minkyu Choi; Zesen Zhao; Kuan Han; Qing Qu 001; Zhongming Liu (CPAL, 2025)

Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning. - Can Yaras; Siyi Chen; Peng Wang 098; Qing Qu 001 (CPAL, 2025)

Sequential Diffusion-Guided Deep Image Prior for Medical Image Reconstruction. - Shijun Liang; Ismail Alkhouri; Qing Qu 001; Rongrong Wang; Saiprasad Ravishankar (ICASSP, 2025)

Learning Dynamics of Deep Matrix Factorization Beyond the Edge of Stability. - Avrajit Ghosh; Soo Min Kwon; Rongrong Wang; Saiprasad Ravishankar; Qing Qu 001 (ICLR, 2025)

Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction. - Shijun Liang; Evan Bell; Qing Qu 001; Rongrong Wang; Saiprasad Ravishankar (IEEE Trans. Computational Imaging, 2025)

Understanding How Nonlinear Layers Create Linearly Separable Features for Low-Dimensional Data. - Alec S. Xu; Can Yaras; Peng Wang 098; Qing Qu 001 (CoRR, 2025)

FlowDAS: A Flow-Based Framework for Data Assimilation. - Siyi Chen; Yixuan Jia; Qing Qu 001; He Sun 010; Jeffrey A. Fessler (CoRR, 2025)

Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling. - Xiao Li; Zekai Zhang; Xiang Li; Siyi Chen; Zhihui Zhu; Peng Wang 098; Qing Qu 001 (CoRR, 2025)

Learning Dynamics of Deep Linear Networks Beyond the Edge of Stability. - Avrajit Ghosh; Soo Min Kwon; Rongrong Wang; Saiprasad Ravishankar; Qing Qu 001 (CoRR, 2025)

An Overview of Low-Rank Structures in the Training and Adaptation of Large Models. - Laura Balzano; Tianjiao Ding; Benjamin D. Haeffele; Soo Min Kwon; Qing Qu 001; Peng Wang 098; Zhangyang Wang; Can Yaras (CoRR, 2025)

The Dual Power of Interpretable Token Embeddings: Jailbreaking Attacks and Defenses for Diffusion Model Unlearning. - Siyi Chen; Yimeng Zhang; Sijia Liu; Qing Qu 001 (CoRR, 2025)

UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weights. - Shijun Liang; Ismail R. Alkhouri; Siddhant Gautam; Qing Qu 001; Saiprasad Ravishankar (CoRR, 2025)

Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective. - Soo Min Kwon; Alec S. Xu; Can Yaras; Laura Balzano; Qing Qu 001 (CoRR, 2025)

Towards Understanding the Mechanisms of Classifier-Free Guidance. - Xiang Li; Rongrong Wang; Qing Qu 001 (CoRR, 2025)

Understanding Generalization in Diffusion Models via Probability Flow Distance. - Huijie Zhang; Zijian Huang 015; Siyi Chen; Jinfan Zhou; Zekai Zhang; Peng Wang 098; Qing Qu 001 (CoRR, 2025)

Attention-Only Transformers via Unrolled Subspace Denoising. - Peng Wang 098; Yifu Lu; Yaodong Yu; Druv Pai; Qing Qu 001; Yi Ma 001 (CoRR, 2025)