Publications

Selected Publications

Bounding training data reconstruction in private (deep) learning [paper][code][talk]

Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten. International Conference on Machine Learning (ICML), 2022. [Long oral]

Measuring data leakage in machine-learning models with Fisher information [paper][code][talk]

Awni Hannun, Chuan Guo, Laurens van der Maaten. Conference on Uncertainty in Artificial Intelligence (UAI), 2021. [Best paper award]

Certified data removal from machine learning models [paper][code][talk]

Chuan Guo, Tom Goldstein, Awni Hannun, Laurens van der Maaten. International Conference on Machine Learning (ICML), 2020.

Simple black-box adversarial attacks [paper][code][talk]

Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger. International Conference on Machine Learning (ICML), 2019.

Countering adversarial images using input transformations [paper][code]

Chuan Guo, Mayank Rana, Moustapha Cisse, Laurens van der Maaten. International Conference on Learning Representations (ICLR), 2018.

On calibration of modern neural networks [paper][code][talk]

Chuan Guo*, Geoff Pleiss*, Yu Sun*, Kilian Q. Weinberger. International Conference on Machine Learning (ICML), 2017.

2023

Learning To Invert: Simple adaptive attacks for gradient inversion in federated learning [paper]

Ruihan Wu, Xiangyu Chen, Chuan Guo, Kilian Q. Weinberger. Conference on Uncertainty in Artificial Intelligence (UAI), 2023.

Analyzing privacy leakage in machine learning via multiple hypothesis testing: A lesson from Fano [paper]

Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi. International Conference on Machine Learning (ICML), 2023.

Cocktail Party Attack: Breaking aggregation-based privacy in federated learning using independent component analysis [paper]

Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, Edward Suh, Moinuddin K. Qureshi, Hsien-Hsin S. Lee. International Conference on Machine Learning (ICML), 2023.

Privacy-aware compression for federated learning through numerical mechanism design [paper][code]

Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Mike Rabbat. International Conference on Machine Learning (ICML), 2023.

Preliminary version presented at NeurIPS International Workshop on Federated Learning, 2022.

Bounding the invertibility of privacy-preserving instance encoding using Fisher information [paper]

Kiwan Maeng*, Chuan Guo*, Sanjay Kariyappa, Edward Suh. ArXiv, 2023.

Preliminary version presented at NeurIPS International Workshop on Federated Learning, 2022.

Do SSL models have déjà vu? A case of unintended memorization in self-supervised learning [paper][code]

Casey Meehan, Florian Bordes, Pascal Vincent, Kamalika Chaudhuri, Chuan Guo. ArXiv, 2023.

Origins of low-dimensional adversarial perturbations [paper]

Elvis Dohmatob, Chuan Guo, Morgane Goibert. International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. [Oral presentation]

Does label differential privacy prevent label inference attacks? [paper]

Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger, Chuan Guo. International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.

2022

EIFFeL: Ensuring integrity for federated learning [paper]

Amrita Roy Chowdhury, Chuan Guo, Somesh Jha, Laurens van der Maaten. ACM Conference on Computer and Communications Security (CCS), 2022.

Privacy-aware compression for federated data analysis [paper][code]

Kamalika Chaudhuri*, Chuan Guo*, Mike Rabbat. Conference on Uncertainty in Artificial Intelligence (UAI), 2022.

Bounding training data reconstruction in private (deep) learning [paper][code][talk]

Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten. International Conference on Machine Learning (ICML), 2022. [Long oral]

SubMix: Practical private prediction for large-scale language models [paper][code]

Antonio A. Ginart, Laurens van der Maaten, James Zou, Chuan Guo. ArXiv, 2022.

On the importance of difficulty calibration in membership inference attacks [paper][code]

Lauren Watson, Chuan Guo, Graham Cormode, Alexandre Sablayrolles. International Conference on Learning Representations (ICLR), 2022.

2021

ReAct: Out-of-distribution detection with rectified activations [paper][code]

Yiyou Sun, Chuan Guo, Yixuan Li. Conference on Neural Information Processing Systems (NeurIPS), 2021.

BulletTrain: Accelerating robust neural network training via boundary example mining [paper]

Weizhe Hua, Yichi Zhang, Chuan Guo, Zhiru Zhang, G. Edward Suh. Conference on Neural Information Processing Systems (NeurIPS), 2021.

Online adaptation to label distribution shift [paper][code]

Ruihan Wu, Chuan Guo, Yi Su, Kilian Q. Weinberger. Conference on Neural Information Processing Systems (NeurIPS), 2021.

Fixes that fail: Self-defeating improvements in machine-learning systems [paper][code]

Ruihan Wu, Chuan Guo, Awni Hannun, Laurens van der Maaten. Conference on Neural Information Processing Systems (NeurIPS), 2021.

Gradient-based adversarial attacks against text transformers [paper][code]

Chuan Guo*, Alexandre Sablayrolles*, Hervé Jégou, Douwe Kiela. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021.

Byzantine-robust and privacy-preserving framework for FedML [paper]

Hanieh Hashemi, Yongqin Wang, Chuan Guo, Murali Annavaram. ICLR Workshop on Safety and Security in Machine Learning Systems, 2021.

Measuring data leakage in machine-learning models with Fisher information [paper][code][talk]

Awni Hannun, Chuan Guo, Laurens van der Maaten. Conference on Uncertainty in Artificial Intelligence (UAI), 2021. [Best paper award]

Making paper reviewing robust to bid manipulation attacks [paper][code][talk]

Ruihan Wu*, Chuan Guo*, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger. International Conference on Machine Learning (ICML), 2021.

Secure multi-party computations in floating-point arithmetic [paper][code]

Chuan Guo, Awni Hannun, Brian Knott, Laurens van der Maaten, Mark Tygert, Ruiyu Zhu. Information and Inference, A Journal of the IMA, iaaa038, 2021. [Alphabetical order]

2016-2020

Certified data removal from machine learning models [paper][code][talk]

Chuan Guo, Tom Goldstein, Awni Hannun, Laurens van der Maaten. International Conference on Machine Learning (ICML), 2020.

On hiding neural networks inside neural networks [paper][code]

Chuan Guo*, Ruihan Wu*, Kilian Q. Weinberger. ArXiv, 2020.

A new defense against adversarial images: Turning a weakness into a strength [paper][code]

Tao Yu*, Shengyuan Hu*, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger. Conference on Neural Information Processing Systems (NeurIPS), 2019.

Breaking the glass ceiling for embedding-based classifiers for large output spaces [paper]

Chuan Guo*, Ali Mousavi*, Xiang Wu, Daniel Holtmann-Rice, Satyen Kale, Sashank Reddi, Sanjiv Kumar. Conference on Neural Information Processing Systems (NeurIPS), 2019.

Low frequency adversarial perturbations [paper][code]

Chuan Guo, Jared S. Frank, Kilian Q. Weinberger. Conference on Uncertainty in Artificial Intelligence (UAI), 2019.

Simple black-box adversarial attacks [paper][code][talk]

Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger. International Conference on Machine Learning (ICML), 2019.

Countering adversarial images using input transformations [paper][code]

Chuan Guo, Mayank Rana, Moustapha Cisse, Laurens van der Maaten. International Conference on Learning Representations (ICLR), 2018.

An empirical study on evaluation metrics of generative adversarial networks [paper][code]

Qiantong Xu, Gao Huang, Yang Yuan, Chuan Guo, Yu Sun, Felix Wu, Kilian Q. Weinberger. ArXiv, 2018.

On calibration of modern neural networks [paper][code][talk]

Chuan Guo*, Geoff Pleiss*, Yu Sun*, Kilian Q. Weinberger. International Conference on Machine Learning (ICML), 2017.

Discovering and exploiting additive structure for Bayesian optimization [paper][code]

Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger Grosse. International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.

Supervised word mover's distance [paper][code][talk]

Gao Huang*, Chuan Guo*, Matt J. Kusner, Yu Sun, Kilian Q. Weinberger, Fei Sha. Conference on Neural Information Processing Systems (NeurIPS), 2016. [Oral presentation]

Theses

Doctoral Thesis: Threats and Countermeasures in Machine Learning Applications [pdf]

Committee: Kilian Q. Weinberger, Karthik Sridharan, Thorsten Joachims

Department of Computer Science, Cornell University, 2020

Master's Thesis: Fingerprinting Codes and Related Combinatorial Structures [pdf]

Committee: Douglas R. Stinson, Alfred Menezes, David Jao

Department of Computer Science, University of Waterloo, 2015

Combinatorics

Chuan Guo, Michael Newman. On b-chromatic numbers of Cartesian products. Discrete Applied Mathematics 239, pp. 82–93, 2018.

Chuan Guo, Douglas R. Stinson. A tight bound on the size of certain separating hash families. Australasian Journal of Combinatorics 67, pp. 294–303, 2017.

Chuan Guo, Jeffrey Shallit, Arseny M. Shur. Palindromic rich words and run-length encodings. Information Processing Letters 116, pp. 735–738, 2016.

Chuan Guo, Douglas R. Stinson, Tran van Trung. On symmetric designs and binary 3-frameproof codes. In Springer Proceedings in Mathematics and Statistics: Algebraic Design Theory and Hadamard Matrices (ADTHM), pp. 125–136, 2015.

Chuan Guo, Douglas R. Stinson, Tran van Trung. On tight bounds for binary frameproof codes. Designs, Codes and Cryptography 77, pp. 301–319, 2015.