Qu, Qing

50 publications

NeurIPS 2025 A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective Lianghe Shi, Meng Wu, Huijie Zhang, Zekai Zhang, Molei Tao, Qing Qu

ICML 2025 Attention-Only Transformers via Unrolled Subspace Denoising Peng Wang, Yifu Lu, Yaodong Yu, Druv Pai, Qing Qu, Yi Ma

ICLRW 2025 Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering Peng Wang, Huijie Zhang, Zekai Zhang, Siyi Chen, Yi Ma, Qing Qu

CPAL 2025 Enhancing Video Representation Learning with Temporal Differentiation Siyi Chen, Minkyu Choi, Zesen Zhao, Kuan Han, Qing Qu, Zhongming Liu

CPAL 2025 Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning Can Yaras, Siyi Chen, Peng Wang, Qing Qu

NeurIPS 2025 FlowDAS: A Stochastic Interpolant-Based Framework for Data Assimilation Siyi Chen, Yixuan Jia, Qing Qu, He Sun, Jeffrey A Fessler

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

ICML 2025 SITCOM: Step-Wise Triple-Consistent Diffusion Sampling for Inverse Problems Ismail Alkhouri, Shijun Liang, Cheng-Han Huang, Jimmy Dai, Qing Qu, Saiprasad Ravishankar, Rongrong Wang

NeurIPS 2025 Shallow Diffuse: Robust and Invisible Watermarking Through Low-Dim Subspaces in Diffusion Models Wenda Li, Huijie Zhang, Qing Qu

NeurIPS 2025 Towards Understanding the Mechanisms of Classifier-Free Guidance Xiang Li, Rongrong Wang, Qing Qu

NeurIPS 2025 UGoDIT: Unsupervised Group Deep Image Prior via Transferable Weights Shijun Liang, Ismail Alkhouri, Siddhant Gautam, Qing Qu, Saiprasad Ravishankar

JMLR 2025 Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination Peng Wang, Xiao Li, Can Yaras, Zhihui Zhu, Laura Balzano, Wei Hu, Qing Qu

NeurIPS 2025 Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling Xiao Li, Zekai Zhang, Xiang Li, Siyi Chen, Zhihui Zhu, Peng Wang, Qing Qu

ICML 2024 A Global Geometric Analysis of Maximal Coding Rate Reduction Peng Wang, Huikang Liu, Druv Pai, Yaodong Yu, Zhihui Zhu, Qing Qu, Yi Ma

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

ICML 2024 Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation Can Yaras, Peng Wang, Laura Balzano, Qing Qu

NeurIPSW 2024 Diffusion Model Learns Low-Dimensional Distributions via Subspace Clustering Peng Wang, Huijie Zhang, Zekai Zhang, Siyi Chen, Yi Ma, Qing Qu

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

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

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

NeurIPS 2024 Image Reconstruction via Autoencoding Sequential Deep Image Prior Ismail R. Alkhouri, Shijun Liang, Evan Bell, Qing Qu, Rongrong Wang, Saiprasad Ravishankar

CVPR 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

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

ICML 2024 Neural Collapse in Multi-Label Learning with Pick-All-Label Loss Pengyu Li, Xiao Li, Yutong Wang, Qing Qu

ICML 2024 Optimal Eye Surgeon: Finding Image Priors Through Sparse Generators at Initialization Avrajit Ghosh, Xitong Zhang, Kenneth K. Sun, Qing Qu, Saiprasad Ravishankar, Rongrong Wang

NeurIPSW 2024 Shallow Diffuse: Robust and Invisible Watermarking Through Low-Dimensional Subspaces in Diffusion Models Wenda Li, Huijie Zhang, Qing Qu

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

ICML 2024 Symmetric Matrix Completion with ReLU Sampling Huikang Liu, Peng Wang, Longxiu Huang, Qing Qu, Laura Balzano

ICML 2024 The Emergence of Reproducibility and Consistency in Diffusion Models Huijie Zhang, Jinfan Zhou, Yifu Lu, Minzhe Guo, Peng Wang, Liyue Shen, Qing Qu

NeurIPSW 2024 Understanding Diffusion-Based Representation Learning via Low-Dimensional Modeling Xiao Li, Zekai Zhang, Xiang Li, Siyi Chen, Zhihui Zhu, Peng Wang, Qing Qu

NeurIPS 2024 Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure Xiang Li, Yixiang Dai, Qing Qu

TMLR 2024 Understanding and Improving Transfer Learning of Deep Models via Neural Collapse Xiao Li, Sheng Liu, Jinxin Zhou, Xinyu Lu, Carlos Fernandez-Granda, Zhihui Zhu, Qing Qu

NeurIPSW 2024 Unfolding Videos Dynamics via Taylor Expansion Siyi Chen, Minkyu Choi, Zesen Zhao, Kuan Han, Qing Qu, Zhongming Liu

NeurIPSW 2023 Invariant Low-Dimensional Subspaces in Gradient Descent for Learning Deep Matrix Factorizations Can Yaras, Peng Wang, Wei Hu, Zhihui Zhu, Laura Balzano, Qing Qu

NeurIPSW 2023 Investigating the Catastrophic Forgetting in Multimodal Large Language Models Yuexiang Zhai, Shengbang Tong, Xiao Li, Mu Cai, Qing Qu, Yong Jae Lee, Yi Ma

NeurIPS 2022 Are All Losses Created Equal: A Neural Collapse Perspective Jinxin Zhou, Chong You, Xiao Li, Kangning Liu, Sheng Liu, Qing Qu, Zhihui Zhu

NeurIPSW 2022 Linear Convergence Analysis of Neural Collapse with Unconstrained Features Peng Wang, Huikang Liu, Can Yaras, Laura Balzano, Qing Qu

NeurIPS 2022 Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold Can Yaras, Peng Wang, Zhihui Zhu, Laura Balzano, Qing Qu

ICML 2022 On the Optimization Landscape of Neural Collapse Under MSE Loss: Global Optimality with Unconstrained Features Jinxin Zhou, Xiao Li, Tianyu Ding, Chong You, Qing Qu, Zhihui Zhu

ICML 2022 Robust Training Under Label Noise by Over-Parameterization Sheng Liu, Zhihui Zhu, Qing Qu, Chong You

NeurIPS 2021 A Geometric Analysis of Neural Collapse with Unconstrained Features Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu

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

NeurIPS 2021 Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery Lijun Ding, Liwei Jiang, Yudong Chen, Qing Qu, Zhihui Zhu

ICLR 2020 Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning Qing Qu, Yuexiang Zhai, Xiao Li, Yuqian Zhang, Zhihui Zhu

NeurIPS 2020 Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-Parameterization Chong You, Zhihui Zhu, Qing Qu, Yi Ma

ICLR 2020 Short and Sparse Deconvolution --- a Geometric Approach Yenson Lau, Qing Qu, Han-Wen Kuo, Pengcheng Zhou, Yuqian Zhang, John Wright

NeurIPS 2019 A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution Qing Qu, Xiao Li, Zhihui Zhu

NeurIPS 2017 Convolutional Phase Retrieval Qing Qu, Yuqian Zhang, Yonina Eldar, John Wright

ICML 2015 Complete Dictionary Recovery Using Nonconvex Optimization Ju Sun, Qing Qu, John Wright

NeurIPS 2014 Finding a Sparse Vector in a Subspace: Linear Sparsity Using Alternating Directions Qing Qu, Ju Sun, John Wright