Unsupervised feature selection for multi-cluster data. - Deng Cai 001; Chiyuan Zhang; Xiaofei He 001 (KDD, 2010)

Orthogonal Projection Analysis. - Binbin Lin; Chiyuan Zhang; Xiaofei He 001 (IScIDE, 2011)

Semi-supervised Regression via Parallel Field Regularization. - Binbin Lin; Chiyuan Zhang; Xiaofei He 001 (NIPS, 2011)

A Variance Minimization Criterion to Feature Selection Using Laplacian Regularization. - Xiaofei He 001; Ming Ji; Chiyuan Zhang; Hujun Bao (IEEE Trans. Pattern Anal. Mach. Intell., 2011)

Multi-task Vector Field Learning. - Binbin Lin; Sen Yang; Chiyuan Zhang; Jieping Ye; Xiaofei He 001 (NIPS, 2012)

Combining Active and Semi-Supervised Learning for Video Compression. - Chiyuan Zhang; Ming Ji (Int. J. Softw. Informatics, 2013)

Parallel vector field embedding. - Binbin Lin; Xiaofei He 001; Chiyuan Zhang; Ming Ji (J. Mach. Learn. Res., 2013)

Image Compression by Learning to Minimize the Total Error. - Chiyuan Zhang; Xiaofei He 001 (IEEE Trans. Circuits Syst. Video Technol., 2013)

A deep representation for invariance and music classification. - Chiyuan Zhang; Georgios Evangelopoulos; Stephen Voinea; Lorenzo Rosasco; Tomaso A. Poggio (ICASSP, 2014)

Word-level invariant representations from acoustic waveforms. - Stephen Voinea; Chiyuan Zhang; Georgios Evangelopoulos; Lorenzo Rosasco; Tomaso A. Poggio (INTERSPEECH, 2014)

Phone classification by a hierarchy of invariant representation layers. - Chiyuan Zhang; Stephen Voinea; Georgios Evangelopoulos; Lorenzo Rosasco; Tomaso A. Poggio (INTERSPEECH, 2014)

Learning An Invariant Speech Representation. - Georgios Evangelopoulos; Stephen Voinea; Chiyuan Zhang; Lorenzo Rosasco; Tomaso A. Poggio (CoRR, 2014)

Discriminative template learning in group-convolutional networks for invariant speech representations. - Chiyuan Zhang; Stephen Voinea; Georgios Evangelopoulos; Lorenzo Rosasco; Tomaso A. Poggio (INTERSPEECH, 2015)

Learning with a Wasserstein Loss. - Charlie Frogner; Chiyuan Zhang; Hossein Mobahi; Mauricio Araya-Polo; Tomaso A. Poggio (NIPS, 2015)

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. - Tianqi Chen; Mu Li 003; Yutian Li; Min Lin; Naiyan Wang; Minjie Wang; Tianjun Xiao; Bing Xu; Chiyuan Zhang; Zheng Zhang 001 (CoRR, 2015)

Training Deep Nets with Sublinear Memory Cost. - Tianqi Chen 001; Bing Xu; Chiyuan Zhang; Carlos Guestrin (CoRR, 2016)

A-Optimal Projection for Image Representation. - Xiaofei He 001; Chiyuan Zhang; Lijun Zhang 005; Xuelong Li 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

Understanding deep learning requires rethinking generalization. - Chiyuan Zhang; Samy Bengio; Moritz Hardt; Benjamin Recht; Oriol Vinyals (ICLR, 2017)

Distributed state-observer-based traffic density estimation of urban freeway network. - Yuqi Guo; Yangzhou Chen; Wei Li; Chiyuan Zhang (ITSC, 2017)

Decentralized State-Observer-Based Traffic Density Estimation of Large-Scale Urban Freeway Network by Dynamic Model. - Yuqi Guo; Yangzhou Chen; Chiyuan Zhang (Inf., 2017)

Machine Theory of Mind. - Neil C. Rabinowitz; Frank Perbet; H. Francis Song; Chiyuan Zhang; S. M. Ali Eslami; Matthew M. Botvinick (ICML, 2018)

Theory of Deep Learning IIb: Optimization Properties of SGD. - Chiyuan Zhang; Qianli Liao; Alexander Rakhlin; Brando Miranda; Noah Golowich; Tomaso A. Poggio (CoRR, 2018)

A Study on Overfitting in Deep Reinforcement Learning. - Chiyuan Zhang; Oriol Vinyals; Rémi Munos; Samy Bengio (CoRR, 2018)

Unrestricted Adversarial Examples. - Tom B. Brown; Nicholas Carlini; Chiyuan Zhang; Catherine Olsson; Paul F. Christiano; Ian J. Goodfellow (CoRR, 2018)

Deep learning and structured data. - Chiyuan Zhang (2018)

A Novel Hierarchical Collaborative Method Based on Multi-objective Optimization for Modularization of Product Platform. - Qihao Wan; Jiaxin Zhao; Jianru Xue; Chiyuan Zhang; Bin He; Heming Zhang 001 (CSCWD, 2019)

Transfusion: Understanding Transfer Learning for Medical Imaging. - Maithra Raghu; Chiyuan Zhang; Jon M. Kleinberg; Samy Bengio (NeurIPS, 2019)

Identity Crisis: Memorization and Generalization under Extreme Overparameterization. - Chiyuan Zhang; Samy Bengio; Moritz Hardt; Yoram Singer (CoRR, 2019)

Transfusion: Understanding Transfer Learning with Applications to Medical Imaging. - Maithra Raghu; Chiyuan Zhang; Jon M. Kleinberg; Samy Bengio (CoRR, 2019)

Identity Crisis: Memorization and Generalization Under Extreme Overparameterization. - Chiyuan Zhang; Samy Bengio; Moritz Hardt; Michael C. Mozer; Yoram Singer (ICLR, 2020)

What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation. - Vitaly Feldman; Chiyuan Zhang (NeurIPS, 2020)

What is being transferred in transfer learning? - Behnam Neyshabur; Hanie Sedghi; Chiyuan Zhang (NeurIPS, 2020)

Exploring the Memorization-Generalization Continuum in Deep Learning. - Ziheng Jiang; Chiyuan Zhang; Kunal Talwar; Michael C. Mozer (CoRR, 2020)

Characterizing Structural Regularities of Labeled Data in Overparameterized Models. - Ziheng Jiang; Chiyuan Zhang; Kunal Talwar; Michael C. Mozer (ICML, 2021)

Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers. - Piotr Teterwak; Chiyuan Zhang; Dilip Krishnan; Michael C. Mozer (ICML, 2021)

Deep Learning with Label Differential Privacy. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Pasin Manurangsi; Chiyuan Zhang (NeurIPS, 2021)

Do Vision Transformers See Like Convolutional Neural Networks? - Maithra Raghu; Thomas Unterthiner; Simon Kornblith; Chiyuan Zhang; Alexey Dosovitskiy (NeurIPS, 2021)

Understanding deep learning (still) requires rethinking generalization. - Chiyuan Zhang; Samy Bengio; Moritz Hardt; Benjamin Recht; Oriol Vinyals (Commun. ACM, 2021)

Pointer Value Retrieval: A new benchmark for understanding the limits of neural network generalization. - Chiyuan Zhang; Maithra Raghu; Jon M. Kleinberg; Samy Bengio (CoRR, 2021)

Deduplicating Training Data Makes Language Models Better. - Katherine Lee; Daphne Ippolito; Andrew Nystrom; Chiyuan Zhang; Douglas Eck; Chris Callison-Burch; Nicholas Carlini (ACL (1), 2022)

Just Fine-tune Twice: Selective Differential Privacy for Large Language Models. - Weiyan Shi; Ryan Shea; Si Chen 008; Chiyuan Zhang; Ruoxi Jia 001; Zhou Yu (EMNLP, 2022)

Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures. - Emmanuel Abbe; Samy Bengio; Elisabetta Cornacchia; Jon M. Kleinberg; Aryo Lotfi; Maithra Raghu; Chiyuan Zhang (NeurIPS, 2022)

The Privacy Onion Effect: Memorization is Relative. - Nicholas Carlini; Matthew Jagielski; Chiyuan Zhang; Nicolas Papernot; Andreas Terzis; Florian Tramèr (NeurIPS, 2022)

Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. - Yao Qin 001; Chiyuan Zhang; Ting Chen; Balaji Lakshminarayanan; Alex Beutel; Xuezhi Wang 002 (NeurIPS, 2022)

Just Fine-tune Twice: Selective Differential Privacy for Large Language Models. - Weiyan Shi; Si Chen 008; Chiyuan Zhang; Ruoxi Jia 001; Zhou Yu (CoRR, 2022)

Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy. - Daphne Ippolito; Florian Tramèr; Milad Nasr; Chiyuan Zhang; Matthew Jagielski; Katherine Lee; Christopher A. Choquette-Choo; Nicholas Carlini (CoRR, 2022)

Are All Layers Created Equal? - Chiyuan Zhang; Samy Bengio; Yoram Singer (J. Mach. Learn. Res., 2022)

Private Ad Modeling with DP-SGD. - Carson Denison; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Krishna Giri Narra; Amer Sinha; Avinash V. Varadarajan; Chiyuan Zhang (AdKDD@KDD, 2023)

Ticketed Learning-Unlearning Schemes. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Ayush Sekhari; Chiyuan Zhang (COLT, 2023)

Quantifying Memorization Across Neural Language Models. - Nicholas Carlini; Daphne Ippolito; Matthew Jagielski; Katherine Lee; Florian Tramèr; Chiyuan Zhang (ICLR, 2023)

Regression with Label Differential Privacy. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Avinash V. Varadarajan; Chiyuan Zhang (ICLR, 2023)

Measuring Forgetting of Memorized Training Examples. - Matthew Jagielski; Om Thakkar 001; Florian Tramèr; Daphne Ippolito; Katherine Lee; Nicholas Carlini; Eric Wallace; Shuang Song 001; Abhradeep Guha Thakurta; Nicolas Papernot; Chiyuan Zhang (ICLR, 2023)

On User-Level Private Convex Optimization. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (ICML, 2023)

Can Neural Network Memorization Be Localized? - Pratyush Maini; Michael Curtis Mozer; Hanie Sedghi; Zachary Chase Lipton; J. Zico Kolter; Chiyuan Zhang (ICML, 2023)

Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy. - Daphne Ippolito; Florian Tramèr; Milad Nasr; Chiyuan Zhang; Matthew Jagielski; Katherine Lee; Christopher A. Choquette-Choo; Nicholas Carlini (INLG, 2023)

Sparsity-Preserving Differentially Private Training of Large Embedding Models. - Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (NeurIPS, 2023)

User-Level Differential Privacy With Few Examples Per User. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (NeurIPS, 2023)

Optimal Unbiased Randomizers for Regression with Label Differential Privacy. - Ashwinkumar Badanidiyuru Varadaraja; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Avinash V. Varadarajan; Chiyuan Zhang (NeurIPS, 2023)

Counterfactual Memorization in Neural Language Models. - Chiyuan Zhang; Daphne Ippolito; Katherine Lee; Matthew Jagielski; Florian Tramèr; Nicholas Carlini (NeurIPS, 2023)

Training Differentially Private Ad Prediction Models With Semi-Sensitive Features. - Lynn Chua; Qiliang Cui; Badih Ghazi; Charlie Harrison; Pritish Kamath; Walid Krichene; Ravi Kumar 001; Pasin Manurangsi; Nicolas Mayoraz; Hema Venkata Krishna Giri Narra; Steffen Rendle; Amer Sinha; et al. (AdKDD@KDD, 2024)

On Convex Optimization with Semi-Sensitive Features. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (COLT, 2024)

LabelDP-Pro: Learning with Label Differential Privacy via Projections. - Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Chiyuan Zhang (ICLR, 2024)

How Private are DP-SGD Implementations? - Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (ICML, 2024)

A 0.002-mm2, 2.9-μW Pulse-Frequency-Modulation based Temperature Sensor with 40-mK Resolution. - Chiyuan Zhang; Nan Chen 003; Douming Hu; Fang Zhu; Yuesheng Pu; Libin Yao (ISCAS, 2024)

Scalable DP-SGD: Shuffling vs. Poisson Subsampling. - Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (NeurIPS, 2024)

Evaluating Copyright Takedown Methods for Language Models. - Boyi Wei; Weijia Shi; Yangsibo Huang; Noah A. Smith; Chiyuan Zhang; Luke Zettlemoyer; Kai Li 001; Peter Henderson 002 (NeurIPS, 2024)

Training Differentially Private Ad Prediction Models with Semi-Sensitive Features. - Lynn Chua; Qiliang Cui; Badih Ghazi; Charlie Harrison; Pritish Kamath; Walid Krichene; Ravi Kumar 001; Pasin Manurangsi; Krishna Giri Narra; Amer Sinha; Avinash V. Varadarajan; Chiyuan Zhang (CoRR, 2024)

How Private is DP-SGD? - Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (CoRR, 2024)

Localizing Paragraph Memorization in Language Models. - Niklas Stoehr; Mitchell Gordon; Chiyuan Zhang; Owen Lewis (CoRR, 2024)

Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning. - Lynn Chua; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Daogao Liu; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (CoRR, 2024)

Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models. - Lynn Chua; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chulin Xie; Chiyuan Zhang (CoRR, 2024)

On Memorization of Large Language Models in Logical Reasoning. - Chulin Xie; Yangsibo Huang; Chiyuan Zhang; Da Yu; Xinyun Chen; Bill Yuchen Lin; Bo Li 026; Badih Ghazi; Ravi Kumar 001 (CoRR, 2024)

Balls-and-Bins Sampling for DP-SGD. - Lynn Chua; Badih Ghazi; Charlie Harrison; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (AISTATS, 2025)

Fantastic Copyrighted Beasts and How (Not) to Generate Them. - Luxi He; Yangsibo Huang; Weijia Shi; Tinghao Xie; Haotian Liu; Yue Wang; Luke Zettlemoyer; Chiyuan Zhang; Danqi Chen 001; Peter Henderson 002 (ICLR, 2025)

Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. - Yangsibo Huang; Daogao Liu; Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Milad Nasr; Amer Sinha; Chiyuan Zhang (ICLR, 2025)

MUSE: Machine Unlearning Six-Way Evaluation for Language Models. - Weijia Shi; Jaechan Lee; Yangsibo Huang; Sadhika Malladi; Jieyu Zhao 001; Ari Holtzman; Daogao Liu; Luke Zettlemoyer; Noah A. Smith; Chiyuan Zhang (ICLR, 2025)

Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards. - Yangsibo Huang; Milad Nasr; Anastasios Angelopoulos; Nicholas Carlini; Wei-Lin Chiang; Christopher A. Choquette-Choo; Daphne Ippolito; Matthew Jagielski; Katherine Lee; Ken Ziyu Liu; Ion Stoica; Florian Tramèr; et al. (CoRR, 2025)

Scaling Laws for Differentially Private Language Models. - Ryan McKenna; Yangsibo Huang; Amer Sinha; Borja Balle; Zachary Charles; Christopher A. Choquette-Choo; Badih Ghazi; Georgios Kaissis; Ravi Kumar 001; Ruibo Liu; Da Yu; Chiyuan Zhang (CoRR, 2025)

Scaling Embedding Layers in Language Models. - Da Yu; Edith Cohen; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Daogao Liu; Chiyuan Zhang (CoRR, 2025)

MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations. - Kaixuan Huang; Jiacheng Guo; Zihao Li; Xiang Ji; Jiawei Ge 003; Wenzhe Li; Yingqing Guo; Tianle Cai; Hui Yuan 002; Runzhe Wang; Yue Wu; Ming Yin; et al. (CoRR, 2025)

Empirical Privacy Variance. - Yuzheng Hu; Fan Wu 011; Ruicheng Xian; Yuhang Liu; Lydia Zakynthinou; Pritish Kamath; Chiyuan Zhang; David A. Forsyth (CoRR, 2025)

Urania: Differentially Private Insights into AI Use. - Daogao Liu; Edith Cohen; Badih Ghazi; Peter Kairouz; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon; Da Yu; Chiyuan Zhang (CoRR, 2025)

Quantifying Cross-Modality Memorization in Vision-Language Models. - Yuxin Wen; Yangsibo Huang; Tom Goldstein; Ravi Kumar 001; Badih Ghazi; Chiyuan Zhang (CoRR, 2025)

Private Hyperparameter Tuning with Ex-Post Guarantee. - Badih Ghazi; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Chiyuan Zhang (CoRR, 2025)