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Manuscripts

  • Lang Huang, Chao Zhang, Hongyang Zhang. "Self-Adaptive Training: Bridging the Supervised and Self-Supervised Learning", IEEE Transactions on Pattern Analysis and Machine Intelligence, major revision. [arXiv] [code]

    Books

  • With Zhouchen Lin (α-β order). “Low Rank Models in Visual Analysis: Theories, Algorithms and Applications”, Academic Press, Elsevier, 2017. [Elsevier link] [Amazon link]

    picture

    Table of Contents

  • Introduction
  • Linear Models (Single Subspace Models, Multiple-Subspace Models, Theoretical Analysis)
  • Non-Linear Models (Kernel Methods, Laplacian and Hyper-Laplacian Methods, Locally Linear Representation, Transformation Invariant Clustering)
  • Optimization Algorithms (Convex Algorithms, Non-Convex Algorithms, Randomized Algorithms)
  • Representative Applications (Video Denoising, Background Modeling, Robust Alignment by Sparse and Low-Rank Decomposition, Transform Invariant Low-Rank Textures, Motion and Image Segmentation, Image Saliency Detection, Partial-Duplicate Image Search, Image Tag Completion and Refinement, Other Applications)
  • Conclusions (Low-Rank Models for Tensorial Data, Nonlinear Manifold Clustering, Randomized Algorithms)
  • Book Chapters

  • With Wieland Brendel et al. "Adversarial Vision Challenge" in The NeurIPS'18 Competition, Springer, 2020. [Springer link]

    Journal Papers

  • With Avrim Blum, Travis Dick, Naren Manoj (α-β order). "Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images", Journal of Machine Learning Research 21, 2020. [pdf] [code]

  • With Maria-Florina Balcan, Yingyu Liang, Zhao Song, David P. Woodruff (α-β order). "Non-Convex Matrix Completion and Related Problems via Strong Duality", Journal of Machine Learning Research 20, 2019. [pdf]

  • Thierry Bouwmans, Sajid Javed, Hongyang Zhang, Zhouchen Lin, Ricardo Otazo. "On the Applications of Robust PCA in Image and Video Processing", Proceedings of the IEEE 106, 2018. [pdf]

  • Hongyang Zhang, Zhouchen Lin, Chao Zhang. "Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis", IEEE Transactions on Information Theory 62, 2016. [pdf]

  • Hongyang Zhang, Zhouchen Lin, Chao Zhang, Junbin Gao. "Relations Among Some Low Rank Subspace Recovery Models", Neural Computation 27, 2015. [pdf]

  • Hongyang Zhang, Zhouchen Lin, Chao Zhang, Junbin Gao. "Robust Latent Low Rank Representation for Subspace Clustering", Neurocomputing 145, 2014. [pdf]

    Conference Papers

  • Yifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma, Yuan Yao. "Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients", Mathematical and Scientific Machine Learning 2021, Lausanne, Switzerland. [pdf] [arXiv] [code]

  • Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri. "A Closer Look at Accuracy vs. Robustness", NeurIPS 2020, Vancouver, Canada. [blog] [arXiv] [code]

  • Lang Huang, Chao Zhang, Hongyang Zhang. "Self-Adaptive Training: beyond Empirical Risk Minimization", NeurIPS 2020, Vancouver, Canada. [arXiv] [code]

  • Hongyang Zhang*, Xiao Yang*, Fangyun Wei* (*equal contributions), Jun Zhu. "Design and Interpretation of Universal Adversarial Patches in Face Detection", ECCV 2020, virtual. [arXiv]

  • With Zhao Song, Ruosong Wang, Lin Yang, Peilin Zhong (α-β order). "Efficient Symmetric Norm Regression via Linear Sketching", NeurIPS 2019, Vancouver, Canada. [arXiv]

  • With Chen Dan, Hong Wang, Yuchen Zhou, Pradeep Ravikumar. "Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation", NeurIPS 2019, Vancouver, Canada. [pdf]

  • Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan. "Theoretically Principled Trade-off between Robustness and Accuracy", ICML 2019 (Long Talk), Long Beach, USA. [arXiv] [code] (Champion of NeurIPS 2018 Adversarial Vision Challenge)

  • Hongyang Zhang, Junru Shao, Ruslan Salakhutdinov. "Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex", AISTATS 2019, Naha, Japan. [arXiv] [code]

  • With Maria-Florina Balcan, Yi Li, David P. Woodruff (α-β order). "Testing Matrix Rank, Optimally", SODA 2019, San Diego, USA. [pdf] [arXiv]

  • With Vasileios Nakos, Xiaofei Shi, David P. Woodruff (α-β order). "Improved Algorithms for Adaptive Compressed Sensing", ICALP 2018, Prague, Czech. [pdf] [arXiv]

  • With Maria-Florina Balcan, Yingyu Liang, David P. Woodruff (α-β order). "Matrix Completion and Related Problems via Strong Duality", ITCS 2018, Cambridge, USA. [arXiv]

  • With Maria-Florina Balcan (α-β order). "Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions", NIPS 2017, Long Beach, USA. [pdf] [arXiv] [spotlight] [poster]

  • Yichong Xu, Hongyang Zhang, Kyle Miller, Aarti Singh, Artur Dubrawski. "Noise-Tolerant Interactive Learning Using Pairwise Comparisons", NIPS 2017, Long Beach, USA. [pdf] [arXiv] [spotlight] [poster]

  • With Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou (α-β order). "Differentially Private Clustering in High-Dimensional Euclidean Spaces", ICML 2017, Sydney, Australia. [pdf] [supp] [code]

  • Hongyang Zhang, Shan You, Zhouchen Lin, Chao Xu. "Fast Compressive Phase Retrieval under Bounded Noise", AAAI 2017, San Francisco, USA. [pdf] [supp]

  • With Maria-Florina Balcan (α-β order). "Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling", NIPS 2016, Barcelona, Spain. [pdf] [supp] [arXiv] [spotlight] [code]

  • With Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab (α-β order). “Learning and 1-bit Compressed Sensing under Asymmetric Noise", COLT 2016, New York, USA. [pdf]

  • Hongyang Zhang, Zhouchen Lin, Chao Zhang, Edward Chang. "Exact Recoverability of Robust PCA via Outlier Pursuit with Tight Recovery Bounds", AAAI 2015, Austin, USA. [pdf] [supp]

  • Xin Shi, Chao Zhang, Fangyun Wei, Hongyang Zhang, Yiyuan She. "Manifold-Regularized Selectable Factor Extraction for Semi-Supervised Image Classification", BMVC 2015, Swansea, UK. [pdf]

  • Hongyang Zhang, Zhouchen Lin, Chao Zhang. "A Counterexample for the Validity of Using Nuclear Norm as A Convex Surrogate of Rank", ECML/PKDD 2013, Prague, Czech. [pdf]

    Thesis

  • "New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications", Ph.D. Thesis, Carnegie Mellon University. [pdf]

    Workshop Papers

  • Fangcheng Liu, Chao Zhang, Hongyang Zhang. "Towards Transferable Adversarial Perturbations with Minimum Norm", ICML 2021 Workshop on Adversarial Machine Learning. [pdf] (Champion of CVPR 2021 Security AI Challenger: Unrestricted Adversarial Attacks on ImageNet)

  • Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri. "A Closer Look at Accuracy vs. Robustness", ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning (selected as spotlight talk). [blog] [arXiv] [code]

  • Lang Huang, Chao Zhang, Hongyang Zhang. "Self-Adaptive Training: beyond Empirical Risk Minimization", ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning. [arXiv] [code]

  • Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan. "Theoretically Principled Trade-off between Robustness and Accuracy", ICML 2019 Workshop on the Security and Privacy of Machine Learning (selected as contributed talk), Long Beach, USA. [full paper]

  • With Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab (α-β order). "Learning and 1-bit Compressed Sensing under Asymmetric Noise", ICML 2016 Workshop on Advances in Non-Convex Analysis and Optimization, New York, USA. [pdf]

  • Simon S. Du, Yichong Xu, Yuan Li, Hongyang Zhang, Aarti Singh, Pulkit Grover. "Novel Quantization Strategies for Linear Prediction with Guarantees", ICML 2016 Workshop on On-Device Intelligence, New York, USA. [pdf]