How Private is DP-SGD?
Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang.
Preprint, 2024.
arXiv:2403.17673
LabelDP-Pro: Learning with Label Differential Privacy via Projections.
Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang.
International Conference on Learning Representations (ICLR), 2024.
PDF
Training Differentially Private Ad Prediction Models with Semi-Sensitive Features.
Lynn Chua, Qiliang Cui, Badih Ghazi, Charlie Harrison, Pritish Kamath, Walid Krichene, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash Varadarajan, Chiyuan Zhang.
The 5th AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI), 2024.
arXiv:2401.15246
Optimal Unbiased Randomizers for Regression with Label Differential Privacy.
Ashwinkumar Badanidiyuru, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V Varadarajan, Chiyuan Zhang.
Advances in Neural Information Processing Systems (NeurIPS), 2023.
arXiv:2312.05659
Sparsity-Preserving Differentially Private Training of Large Embedding Models.
Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang.
Advances in Neural Information Processing Systems (NeurIPS), 2023.
arXiv:2311.08357
User-Level Differential Privacy With Few Examples Per User.
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang.
Advances in Neural Information Processing Systems (NeurIPS, Oral), 2023.
arXiv:2309.12500
Counterfactual Memorization in Neural Language Models.
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini.
Advances in Neural Information Processing Systems (NeurIPS, Spotlight), 2023.
arXiv:2112.12938
Ticketed Learning-Unlearning Schemes.
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang.
Conference on Learning Theory (COLT), 2023.
arXiv:2306.15744
Can Neural Network Memorization Be Localized?
Pratyush Maini, Michael C. Mozer, Hanie Sedghi, Zachary Lipton, Zico Kolter, Chiyuan Zhang.
International Conference on Machine Learning (ICML), 2023.
arXiv:2307.09542
On User-Level Private Convex Optimization.
Badih Ghazi, Pritish Kamath, Ravi Kumar, Raghu Meka, Pasin Manurangsi, Chiyuan Zhang.
International Conference on Machine Learning (ICML), 2023.
arXiv:2305.04912
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.
International Natural Language Generation Conference (INLG), 2023.
arXiv:2210.17546
Private Ad Modeling with DP-SGD.
Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash Varadarajan, Chiyuan Zhang.
AdKDD, 2023.
arXiv:2211.11896
Regression with Label Differential Privacy.
Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang.
The International Conference on Learning Representations (ICLR), 2023.
arXiv:2212.06074
Quantifying Memorization Across Neural Language Models.
Nicholas Carlini♮, Daphne Ippolito♮, Matthew Jagielski♮, Katherine Lee♮, Florian Tramer♮, Chiyuan Zhang♮.
♮equal contribution.
The International Conference on Learning Representations (ICLR, top-25%), 2023.
arXiv:2202.07646
Data
Measuring Forgetting of Memorized Training Examples.
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang.
The International Conference on Learning Representations (ICLR), 2023.
arXiv:2207.00099
Just Fine-tune Twice: Selective Differential Privacy for Large Language Models.
Weiyan Shi, Si Chen, Chiyuan Zhang, Ruoxi Jia, Zhou Yu.
Empirical Methods in Natural Language Processing (EMNLP), 2022.
arXiv:2204.07667
The Privacy Onion Effect: Memorization is Relative.
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramer.
Advances in Neural Information Processing Systems (NeurIPS), 2022.
arXiv:2206.10469
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures?
Emmanuel Abbe, Samy Bengio, Elisabetta Cornacchia, Jon Kleinberg, Aryo Lotfi, Maithra Raghu, Chiyuan Zhang.
Advances in Neural Information Processing Systems (NeurIPS), 2022.
arXiv:2205.13647
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation.
Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang.
Advances in Neural Information Processing Systems (NeurIPS), 2022.
arXiv:2110.07858
Are All Layers Created Equal?
Chiyuan Zhang, Samy Bengio, Yoram Singer.
Journal of Machine Learning Research (JMLR), 2022.
arXiv:1902.01996
JMLR
Deduplicating Training Data Makes Language Models Better.
Katherine Lee♮, Daphne Ippolito♮, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini.
♮equal contribution.
The 60th Annual Meeting of the Association for Computational Linguistics (ACL, Oral), 2022.
arXiv:2107.06499
code
Do Vision Transformers See Like Convolutional Neural Networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy.
Advances in Neural Information Processing Systems (NeurIPS),
2021.
arXiv:2108.08810
Deep Learning with Label Differential Privacy.
Badih Ghazi♮, Noah Golowich♮, Ravi Kumar♮, Pasin Manurangsi♮, Chiyuan Zhang♮.
♮equal contribution.
Advances in Neural Information Processing Systems (NeurIPS),
2021.
arXiv:2102.06062
code
Characterizing Structural Regularities of Labeled Data in Overparameterized Models.
Ziheng Jiang♮, Chiyuan Zhang♮, Kunal Talwar, Michael C. Mozer.
♮equal contribution.
International Conference on Machine Learning (ICML, Long presentation (3%)), 2021.
arXiv:2002.03206
project website
code
Understanding invariance via feedforward inversion of discriminatively trained classifiers.
Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan, Michael C. Mozer.
International Conference on Machine Learning (ICML), 2021.
arXiv:2103.07470
project website
Understanding Deep Learning (Still) Requires Rethinking Generalization.
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals.
Communications of the ACM, March 2021, Vol. 64 No. 3, Pages 107-115.
(Republication of our ICLR 2017 papar as CACM Research Highlights).
Full Article
Media
Technical Perspective
What is being transferred in transfer learning?.
Behnam Neyshabur♮, Hanie Sedghi♮, Chiyuan Zhang♮.
♮equal contribution.
Advances in Neural Information Processing Systems (NeurIPS), 2020.
arXiv:2008.11687
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation.
Vitaly Feldman♮, Chiyuan Zhang♮.
♮equal contribution.
Advances in Neural Information Processing Systems (NeurIPS, Spotlight (4%)), 2020.
arXiv:2008.03703
project website
Identity Crisis: Memorization and Generalization under Extreme Overparameterization.
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Michael C. Mozer, Yoram Singer.
The International Conference on Learning Representations (ICLR), 2020.
arXiv:1902.04698
Transfusion: Understanding Transfer Learning for Medical Imaging.
Maithra Raghu♮, Chiyuan Zhang♮, Jon Kleinberg♭, Samy Bengio♭.
♮equal contribution; ♭equal contribution.
Advances in Neural Information Processing Systems (NeurIPS), 2019.
arXiv:1902.07208
Unrestricted Adversarial Examples.
Tom B. Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul Christiano, Ian Goodfellow.
Preprint 2018.
arXiv:1809.08352
A Study on Overfitting in Deep Reinforcement Learning.
Chiyuan Zhang, Oriol Vinyals, Remi Munos, Samy Bengio.
Preprint 2018.
arXiv:1804.06893
Machine Theory of Mind.
Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S.M. Ali Eslami, Matthew Botvinick.
International Conference on Machine Learning (ICML), 2018.
arXiv:1802.07740
Automated fault detection without seismic processing.
Mauricio Araya-Polo, Taylor Dahlke, Charlie Frogner, Chiyuan Zhang, Tomaso Poggio, and Detlef Hohl.
The Leading Edge (TLE), Society of Exploration Geophysicists (SEG), 2017.
DOI:10.1190/tle36030208.1
Understanding Deep Learning Requires Rethinking Generalization.
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals.
International Conference on Learning Representations (ICLR), Best Paper Award, 2017.
arXiv:1611.03530
code
Training Deep Nets with Sublinear Memory Cost.
Tianqi Chen, Bing Xu, Chiyuan Zhang, Carlos Guestrin.
Preprint 2016.
arXiv:1604.06174
code
Learning with a Wasserstein Loss.
Charlie Frogner♮, Chiyuan Zhang♮, Hossein Mobahi, Mauricio Araya-Polo, Tomaso Poggio.
♮equal contribution.
Advances in Neural Information Processing Systems (NeurIPS), 2015.
arXiv:1506.05439
project website
MIT News
MXNet: A Distributed Deep Learning Framework for Efficiency and Flexibility.
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang,
Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, Zheng Zhang.
NeurIPS Workshop on LearningSys, 2015.
arXiv:1512.01274
code
A-Optimal Projection for Image Representation.
Xiaofei He, Chiyuan Zhang, Lijun Zhang, Xuelong Li.
IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 2015.
Discriminative Template Learning in Group-Convolutional Networks for Invariant Speech Representations.
Chiyuan Zhang, Stephen Voinea, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso Poggio.
INTERSPEECH 2015.
Phone Classification by a Hierarchy of Invariant Representation Layers.
Chiyuan Zhang, Stephen Voinea, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso Poggio.
INTERSPEECH 2014.
Word-level Invariant Representations from Acoustic Waveforms.
Stephen Voinea, Chiyuan Zhang, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso Poggio.
INTERSPEECH, Best Student Paper, 2014.
Learning An Invariant Speech Representation.
Georgios Evangelopoulos, Stephen Voinea, Chiyuan Zhang, Lorenzo Rosasco, Tomaso Poggio.
CBMM Memo No. 22, 2014.
arXiv:1406.3884
A Deep Representation for Invariance and Music Classification.
Chiyuan Zhang, Georgios Evangelopoulos, Stephen Voinea, Lorenzo Rosasco, Tomaso Poggio.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014.
Machine-learning Based Automated Fault Detection in Seismic Traces.
Chiyuan Zhang, Charlie Frogner, Mauricio Araya-Polo, Detlef Hohl.
Proceedings of 76th European Association of Geoscientists and Engineers Conference & Exhibition (EAGE), 2014.
PDF
Parallel Vector Field Embedding.
Binbin Lin, Xiaofei He, Chiyuan Zhang, Ming Ji.
Journal of Machine Learning Research (JMLR), 2013.
JMLR
Image Compression by Learning to Minimize the Total Error.
Chiyuan Zhang, Xiaofei He.
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2013.
Multi-task Vector Field Learning.
Binbin Lin, Sen Yang, Chiyuan Zhang, Jieping Ye, Xiaofei He.
Advances in Neural Information Processing Systems (NeurIPS), 2012.
paper
Semi-supervised Regression via Parallel Field Regularization.
Binbin Lin, Chiyuan Zhang, Xiaofei He.
Advances in Neural Information Processing Systems (NeurIPS), 2011.
paper
A Variance Minimization Criterion to Feature Selection using Laplacian Regularization.
Xiaofei He, Ming Ji, Chiyuan Zhang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2011.
Unsupervised feature selection for multi-cluster data.
Deng Cai, Chiyuan Zhang, Xiaofei He.
Proc. of the 16th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), 2010.