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Zhouchen Lin, Hongyang Zhang. “Low Rank Models in Visual Analysis: Theories, Algorithms and Applications”, Academic Press, Elsevier, 2017. [Elsevier link] [Amazon link]
Wieland Brendel, Hongyang Zhang et al. "Adversarial Vision Challenge" in The NeurIPS'18 Competition, Springer, 2020. [Springer link]
Haochen Sun, Tonghe, Bai, Jason Li, Hongyang Zhang. "zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training", IEEE Transactions on Information Forensics & Security, 2024. [arXiv]
Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang. "An Analysis of Robustness of Non-Lipschitz Networks", Journal of Machine Learning Research, 2023. [pdf]
Zhuangfei Hu, Xinda Li, David P. Woodruff, Hongyang Zhang, Shufan Zhang. "Recovery from Non-Decomposable Distance Oracles", IEEE Transactions on Information Theory, 2023. [arXiv]
Lang Huang, Chao Zhang, Hongyang Zhang. "Self-Adaptive Training: Bridging the Supervised and Self-Supervised Learning", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. [arXiv] [code]
Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang. "Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images", Journal of Machine Learning Research 21, 2020. [pdf] [code]
Maria-Florina Balcan, Yingyu Liang, Zhao Song, David P. Woodruff, Hongyang Zhang. "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]
Fangyun Wei, Jinjing Zhao, Kun Yan, Hongyang Zhang, Chang Xu. "A Large-Scale Human-Centric Benchmark for Referring Expression Comprehension in the LMM Era", NeurIPS 2024 (Datasets and Benchmarks Track), Vancouver, Canada. [pdf] [code]
Yuhui Li, Fangyun Wei, Chao Zhang, Hongyang Zhang. "EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees", EMNLP 2024, Miami, USA. [arXiv] [code]
Yuhui Li, Zejia Wu, Chao Zhang, Hongyang Zhang. "Direct-Effect Risk Minimization for Domain Generalization", ECML PKDD 2024, Vilnius, Lithuania. [arXiv] [code]
Yu Du, Fangyun Wei, Hongyang Zhang. "AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls", ICML 2024, Vienna, Austria. [arXiv] [code]
Yuhui Li, Fangyun Wei, Chao Zhang, Hongyang Zhang. "EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty", ICML 2024, Vienna, Austria. [arXiv] [code]
Yihan Wu, Zhengmian Hu, Hongyang Zhang, Heng Huang. "DiPmark: A Stealthy, Efficient and Resilient Watermark for Large Language Models", ICML 2024, Vienna, Austria. [arXiv]
Haochen Sun, Jason Li, Hongyang Zhang. "zkLLM: Zero Knowledge Proofs for Large Language Models", ACM CCS 2024, Salt Lake City, USA. [pdf]
Yuhui Li, Fangyun Wei, Jinjing Zhao, Chao Zhang, Hongyang Zhang. "RAIN: Your Language Models Can Align Themselves without Finetuning", ICLR 2024, Vienna, Austria. [arXiv]
Zhengmian Hu, Lichang Chen, Xidong Wu, Yihan Wu, Hongyang Zhang, Heng Huang. "Unbiased Watermark for Large Language Models", ICLR 2024 (Spotlight), Vienna, Austria. [arXiv] [code] [机器之心]
Yimu Wang, Yihan Wu, and Hongyang Zhang. "Lost Domain Generalization Is a Natural Consequence of Lack of Training Domains", AAAI 2024, Vancouver, Canada. [pdf]
Yihan Wu, Heng Huang, Hongyang Zhang. "A Law of Robustness beyond Isoperimetry", ICML 2023, Hawaii, USA. [pdf]
Yuzheng Hu, Fan Wu, Hongyang Zhang, Han Zhao. "Understanding the Impact of Adversarial Robustness on Accuracy Disparity", ICML 2023, Hawaii, USA. [arXiv]
Yimu Wang, Dinghuai Zhang, Yihan Wu, Heng Huang, Hongyang Zhang. "Cooperation or Competition: Avoiding Player Domination for Multi-target Robustness by Adaptive Budgets", CVPR 2023, Vancouver, Canada. [pdf]
Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang. "Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games", AISTATS 2023, Valencia, Spain. [arXiv]
Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang. "Causal Balancing for Domain Generalization", ICLR 2023, Kigali, Rwanda. [arXiv]
Fangcheng Liu, Chao Zhang, Hongyang Zhang. "Towards Transferable Unrestricted Adversarial Examples with Minimum Changes", IEEE SaTML 2023, Raleigh, USA. [arXiv] [code] (Champion of CVPR 2021 Security AI Challenger: Unrestricted Adversarial Attacks on ImageNet)
Zhuangfei Hu, Xinda Li, David P. Woodruff, Hongyang Zhang, Shufan Zhang. "Recovery from Non-Decomposable Distance Oracles", ITCS 2023, Cambridge, USA. [arXiv]
Minghan Li, Xinyu Zhang, Ji Xin, Hongyang Zhang, Jimmy Lin. "Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking", EMNLP 2022 (Oral), Abu Dhabi, United Arab Emirates. [arXiv]
Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang. "Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness", NeurIPS 2022, New Orleans, USA. [arXiv]
Yihan Wu, Hongyang Zhang, Heng Huang. "RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval", ICML 2022, Baltimore, USA. [arXiv]
Dinghuai Zhang, Hongyang Zhang, Aaron Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala. "Building Robust Ensembles via Margin Boosting", ICML 2022, Baltimore, USA. [arXiv]
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]
Zhao Song, Ruosong Wang, Lin Yang, Hongyang Zhang, Peilin Zhong. "Efficient Symmetric Norm Regression via Linear Sketching", NeurIPS 2019, Vancouver, Canada. [arXiv]
Chen Dan, Hong Wang, Yuchen Zhou, Pradeep Ravikumar, Hongyang Zhang. "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]
Maria-Florina Balcan, Yi Li, David P. Woodruff, Hongyang Zhang. "Testing Matrix Rank, Optimally", SODA 2019, San Diego, USA. [pdf] [arXiv]
Vasileios Nakos, Xiaofei Shi, David P. Woodruff, Hongyang Zhang. "Improved Algorithms for Adaptive Compressed Sensing", ICALP 2018, Prague, Czech. [pdf] [arXiv]
Maria-Florina Balcan, Yingyu Liang, David P. Woodruff, Hongyang Zhang. "Matrix Completion and Related Problems via Strong Duality", ITCS 2018, Cambridge, USA. [arXiv]
Maria-Florina Balcan, Hongyang Zhang. "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]
Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang. "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]
Maria-Florina Balcan, Hongyang Zhang. "Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling", NIPS 2016, Barcelona, Spain. [pdf] [supp] [arXiv] [spotlight] [code]
Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Hongyang Zhang. “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]
"New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications", Ph.D. Thesis, Carnegie Mellon University. [pdf]
Haochen Sun, Hongyang Zhang. "PoT: Securely Proving Legitimacy of Training Data and Logic for AI Regulation", ICML 2023 Workshop on Generative AI and Law. [pdf]
Yimu Wang, Dinghuai Zhang, Yihan Wu, Heng Huang, Hongyang Zhang. "Cooperation or Competition: Avoiding Player Domination for Multi-target Robustness by Adaptive Budgets", NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning. [pdf]
Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang. "On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness", ICLR 2022 Workshop on Socially Responsible Machine Learning (selected as contributed talk). [arXiv]
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 contributed 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, Long Beach, USA. [full paper] (Champion of NeurIPS 2018 Adversarial Vision Challenge, selected as contributed talk)
Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Hongyang Zhang. "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]
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