A Counterexample for the Validity of Using Nuclear Norm as a Convex Surrogate of Rank. - Hongyang Zhang 001; Zhouchen Lin; Chao Zhang 001 (ECML/PKDD (2), 2013)

Robust latent low rank representation for subspace clustering. - Hongyang Zhang 001; Zhouchen Lin; Chao Zhang 001; Junbin Gao (Neurocomputing, 2014)

Exact Recoverability of Robust PCA via Outlier Pursuit with Tight Recovery Bounds. - Hongyang Zhang 001; Zhouchen Lin; Chao Zhang 001; Edward Y. Chang (AAAI, 2015)

Manifold-Regularized Selectable Factor Extraction for Semi-supervised Image Classification. - Xin Shi; Chao Zhang 001; Fangyun Wei; Hongyang Zhang 001; Yiyuan She (BMVC, 2015)

Relations Among Some Low-Rank Subspace Recovery Models. - Hongyang Zhang 001; Zhouchen Lin; Chao Zhang 001; Junbin Gao (Neural Comput., 2015)

Learning and 1-bit Compressed Sensing under Asymmetric Noise. - Pranjal Awasthi; Maria-Florina Balcan; Nika Haghtalab; Hongyang Zhang 001 (COLT, 2016)

Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling. - Maria-Florina Balcan; Hongyang Zhang 001 (NIPS, 2016)

Completing Low-Rank Matrices With Corrupted Samples From Few Coefficients in General Basis. - Hongyang Zhang 001; Zhouchen Lin; Chao Zhang 001 (IEEE Trans. Inf. Theory, 2016)

Fast Compressive Phase Retrieval under Bounded Noise. - Hongyang Zhang 001; Shan You; Zhouchen Lin; Chao Xu 006 (AAAI, 2017)

Differentially Private Clustering in High-Dimensional Euclidean Spaces. - Maria-Florina Balcan; Travis Dick; Yingyu Liang; Wenlong Mou; Hongyang Zhang 001 (ICML, 2017)

Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions. - Maria-Florina Balcan; Hongyang Zhang 001 (NIPS, 2017)

Noise-Tolerant Interactive Learning Using Pairwise Comparisons. - Yichong Xu; Hongyang Zhang 001; Aarti Singh; Artur Dubrawski; Kyle Miller 001 (NIPS, 2017)

Optimal Sample Complexity for Matrix Completion and Related Problems via 𝓁s2-Regularization. - Maria-Florina Balcan; Yingyu Liang; David P. Woodruff; Hongyang Zhang 001 (CoRR, 2017)

S-Concave Distributions: Towards Broader Distributions for Noise-Tolerant and Sample-Efficient Learning Algorithms. - Maria-Florina Balcan; Hongyang Zhang 001 (CoRR, 2017)

Improved Algorithms for Adaptive Compressed Sensing. - Vasileios Nakos; Xiaofei Shi; David P. Woodruff; Hongyang Zhang 001 (ICALP, 2018)

Matrix Completion and Related Problems via Strong Duality. - Maria-Florina Balcan; Yingyu Liang; David P. Woodruff; Hongyang Zhang 001 (ITCS, 2018)

Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex. - Hongyang Zhang 001; Junru Shao; Ruslan Salakhutdinov (CoRR, 2018)

Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures. - Hongyang Zhang 001; Susu Xu; Jiantao Jiao; Pengtao Xie; Ruslan Salakhutdinov; Eric P. Xing (CoRR, 2018)

On the Applications of Robust PCA in Image and Video Processing. - Thierry Bouwmans; Sajid Javed; Hongyang Zhang 001; Zhouchen Lin; Ricardo Otazo (Proc. IEEE, 2018)

Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex. - Hongyang Zhang 001; Junru Shao; Ruslan Salakhutdinov (AISTATS, 2019)

Theoretically Principled Trade-off between Robustness and Accuracy. - Hongyang Zhang 001; Yaodong Yu; Jiantao Jiao; Eric P. Xing; Laurent El Ghaoui; Michael I. Jordan (ICML, 2019)

Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation. - Chen Dan 001; Hong Wang; Hongyang Zhang 001; Yuchen Zhou; Pradeep Ravikumar (NeurIPS, 2019)

Efficient Symmetric Norm Regression via Linear Sketching. - Zhao Song 002; Ruosong Wang; Lin F. Yang; Hongyang Zhang 001; Peilin Zhong (NeurIPS, 2019)

Testing Matrix Rank, Optimally. - Maria-Florina Balcan; Yi Li 002; David P. Woodruff; Hongyang Zhang 001 (SODA, 2019)

Design and Interpretation of Universal Adversarial Patches in Face Detection. - Xiao Yang; Fangyun Wei; Hongyang Zhang 001; Xiang Ming; Jun Zhu 001 (CoRR, 2019)

Non-Convex Matrix Completion and Related Problems via Strong Duality. - Maria-Florina Balcan; Yingyu Liang; Zhao Song 002; David P. Woodruff; Hongyang Zhang 001 (J. Mach. Learn. Res., 2019)

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications. - Hongyang Zhang 001 (2019)

Design and Interpretation of Universal Adversarial Patches in Face Detection. - Xiao Yang; Fangyun Wei; Hongyang Zhang 001; Jun Zhu 001 (ECCV (17), 2020)

Self-Adaptive Training: beyond Empirical Risk Minimization. - Lang Huang 001; Chao Zhang 001; Hongyang Zhang 001 (NeurIPS, 2020)

A Closer Look at Accuracy vs. Robustness. - Yao-Yuan Yang; Cyrus Rashtchian; Hongyang Zhang 001; Ruslan Salakhutdinov; Kamalika Chaudhuri (NeurIPS, 2020)

Random Smoothing Might be Unable to Certify 𝓁∞ Robustness for High-Dimensional Images. - Avrim Blum; Travis Dick; Naren Manoj; Hongyang Zhang 001 (CoRR, 2020)

Adversarial Robustness Through Local Lipschitzness. - Yao-Yuan Yang; Cyrus Rashtchian; Hongyang Zhang 001; Ruslan Salakhutdinov; Kamalika Chaudhuri (CoRR, 2020)

Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients. - Yifei Huang; Yaodong Yu; Hongyang Zhang 001; Yi Ma 001; Yuan Yao (CoRR, 2020)

On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness. - Maria-Florina Balcan; Avrim Blum; Dravyansh Sharma; Hongyang Zhang 001 (CoRR, 2020)

Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images. - Avrim Blum; Travis Dick; Naren Manoj; Hongyang Zhang 001 (J. Mach. Learn. Res., 2020)

Adversarial Robustness of Stabilized Neural ODE Might be from Obfuscated Gradients. - Yifei Huang; Yaodong Yu; Hongyang Zhang 001; Yi Ma 001; Yuan Yao (MSML, 2021)

Unrestricted Adversarial Attacks on ImageNet Competition. - Yuefeng Chen; Xiaofeng Mao; Yuan He 011; Hui Xue 001; Chao Li; Yinpeng Dong; Qi-An Fu; Xiao Yang; Wenzhao Xiang 001; Tianyu Pang; Hang Su 006; Jun Zhu 001; et al. (CoRR, 2021)

Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking. - Minghan Li 002; Xinyu Zhang 018; Ji Xin; Hongyang Zhang 001; Jimmy Lin (EMNLP, 2022)

RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval. - Yihan Wu; Hongyang Zhang 001; Heng Huang (ICML, 2022)

Building Robust Ensembles via Margin Boosting. - Dinghuai Zhang; Hongyang Zhang 001; Aaron C. Courville; Yoshua Bengio; Pradeep Ravikumar; Arun Sai Suggala (ICML, 2022)

Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness. - Avrim Blum; Omar Montasser; Greg Shakhnarovich; Hongyang Zhang 001 (NeurIPS, 2022)

How Many Data Are Needed for Robust Learning? - Hongyang Zhang 001; Yihan Wu; Heng Huang (CoRR, 2022)

A Closer Look at Robustness to L-infinity and Spatial Perturbations and their Composition. - Luke Rowe; Benjamin Thérien; Krzysztof Czarnecki 001; Hongyang Zhang 001 (CoRR, 2022)

Towards Robust Dataset Learning. - Yihan Wu; Xinda Li 001; Florian Kerschbaum; Heng Huang; Hongyang Zhang 001 (CoRR, 2022)

Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. - Maria-Florina Balcan; Rattana Pukdee; Pradeep Ravikumar; Hongyang Zhang 001 (AISTATS, 2023)

Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets. - Yimu Wang; Dinghuai Zhang; Yihan Wu; Heng Huang; Hongyang Zhang 001 (CVPR, 2023)

Causal Balancing for Domain Generalization. - Xinyi Wang 003; Michael Saxon; Jiachen Li; Hongyang Zhang 001; Kun Zhang 001; William Yang Wang (ICLR, 2023)

Understanding the Impact of Adversarial Robustness on Accuracy Disparity. - Yuzheng Hu; Fan Wu; Hongyang Zhang 001; Han Zhao 002 (ICML, 2023)

A Law of Robustness beyond Isoperimetry. - Yihan Wu; Heng Huang; Hongyang Zhang 001 (ICML, 2023)

Towards Transferable Unrestricted Adversarial Examples with Minimum Changes. - Fangcheng Liu; Chao Zhang 001; Hongyang Zhang 001 (SaTML, 2023)

Investigating the Existence of "Secret Language" in Language Models. - Yimu Wang; Peng Shi; Hongyang Zhang 001 (CoRR, 2023)

DiPmark: A Stealthy, Efficient and Resilient Watermark for Large Language Models. - Yihan Wu; Zhengmian Hu; Hongyang Zhang 001; Heng Huang (CoRR, 2023)

zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training. - Haochen Sun 001; Hongyang Zhang 001 (IACR Cryptol. ePrint Arch., 2023)

An Analysis of Robustness of Non-Lipschitz Networks. - Maria-Florina Balcan; Avrim Blum; Dravyansh Sharma; Hongyang Zhang 001 (J. Mach. Learn. Res., 2023)

Recovery From Non-Decomposable Distance Oracles. - Zhuangfei Hu; Xinda Li 001; David P. Woodruff; Hongyang Zhang 001; Shufan Zhang 001 (IEEE Trans. Inf. Theory, 2023)

Lost Domain Generalization Is a Natural Consequence of Lack of Training Domains. - Yimu Wang; Yihan Wu; Hongyang Zhang 001 (AAAI, 2024)

zkLLM: Zero Knowledge Proofs for Large Language Models. - Haochen Sun 001; Jason Li; Hongyang Zhang 001 (CCS, 2024)

EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees. - Yuhui Li; Fangyun Wei; Chao Zhang 001; Hongyang Zhang 001 (EMNLP, 2024)

Unbiased Watermark for Large Language Models. - Zhengmian Hu; Lichang Chen; Xidong Wu; Yihan Wu; Hongyang Zhang 001; Heng Huang (ICLR, 2024)

RAIN: Your Language Models Can Align Themselves without Finetuning. - Yuhui Li; Fangyun Wei; Jinjing Zhao; Chao Zhang 001; Hongyang Zhang 001 (ICLR, 2024)

AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls. - Yu Du; Fangyun Wei; Hongyang Zhang 001 (ICML, 2024)

EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty. - Yuhui Li; Fangyun Wei; Chao Zhang 001; Hongyang Zhang 001 (ICML, 2024)

A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models. - Yihan Wu; Zhengmian Hu; Junfeng Guo; Hongyang Zhang 001; Heng Huang (ICML, 2024)

Direct-Effect Risk Minimization for Domain Generalization. - Yuhui Li; Zejia Wu; Chao Zhang 001; Hongyang Zhang 001 (ECML/PKDD (3), 2024)

Distortion-free Watermarks are not Truly Distortion-free under Watermark Key Collisions. - Yihan Wu; Ruibo Chen; Zhengmian Hu; Yanshuo Chen; Junfeng Guo; Hongyang Zhang 001; Heng Huang (CoRR, 2024)

Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning. - Lang Huang 001; Chao Zhang 001; Hongyang Zhang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training. - Haochen Sun 001; Tonghe Bai; Jason Li; Hongyang Zhang 001 (IEEE Trans. Inf. Forensics Secur., 2025)