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Chuan Guo 0001
郭川
Person information
- unicode name: 郭川
- affiliation: Meta AI, Menlo Park, CA, USA
- affiliation (PhD 2020): Cornell University, Ithaca, NY, USA
- affiliation (former): University of Waterloo, ON, Canada
Other persons with the same name
- Chuan Guo 0002
— Snap Research, New York, NY, USA (and 1 more)
- Chuan Guo 0003 — Nanchang Hangkong University, China
- Chuan Guo 0004
- Chuan Guo 0005
- Chuan Guo 0006 — Zhejiang Integrated Traditional and Western Medicine Hospital, Hangzhou, China
- Chuan Guo 0007 — PLA Unit No.92538, Dalian, China
- Chuan Guo 0008 — Southern Illinois University Edwardsville, Department of Mechanical and Mechatronics Engineering, IL, USA
- Chuan Guo 0009 — Huawei Technologies Co., Ltd.
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2020 – today
- 2024
- [c33]Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
Differentially Private Representation Learning via Image Captioning. ICML 2024 - [c32]Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
ViP: A Differentially Private Foundation Model for Computer Vision. ICML 2024 - [c31]Trishita Tiwari, Suchin Gururangan, Chuan Guo, Weizhe Hua, Sanjay Kariyappa, Udit Gupta, Wenjie Xiong, Kiwan Maeng, Hsien-Hsin S. Lee, G. Edward Suh:
Information Flow Control in Machine Learning through Modular Model Architecture. USENIX Security Symposium 2024 - [i46]Bargav Jayaraman, Chuan Guo, Kamalika Chaudhuri:
Déjà Vu Memorization in Vision-Language Models. CoRR abs/2402.02103 (2024) - [i45]Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
Differentially Private Representation Learning via Image Captioning. CoRR abs/2403.02506 (2024) - [i44]Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo:
Privacy Amplification for the Gaussian Mechanism via Bounded Support. CoRR abs/2403.05598 (2024) - [i43]Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi, Adriana Romero-Soriano, Kamalika Chaudhuri, Mike Rabbat, Chuan Guo:
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning. CoRR abs/2403.14421 (2024) - [i42]Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert:
Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds. CoRR abs/2404.02866 (2024) - [i41]Anselm Paulus, Arman Zharmagambetov, Chuan Guo, Brandon Amos, Yuandong Tian:
AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs. CoRR abs/2404.16873 (2024) - [i40]Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay, Alexander C. Li, Adrien Bardes, Suzanne Petryk, Oscar Mañas, Zhiqiu Lin, Anas Mahmoud, Bargav Jayaraman, Mark Ibrahim, Melissa Hall, Yunyang Xiong, Jonathan Lebensold, Candace Ross, Srihari Jayakumar, Chuan Guo, Diane Bouchacourt, Haider Al-Tahan, Karthik Padthe, Vasu Sharma, Hu Xu, Xiaoqing Ellen Tan, Megan Richards, Samuel Lavoie, Pietro Astolfi, Reyhane Askari Hemmat, Jun Chen, Kushal Tirumala, Rim Assouel, Mazda Moayeri, Arjang Talattof, Kamalika Chaudhuri, Zechun Liu, Xilun Chen, Quentin Garrido, Karen Ullrich, Aishwarya Agrawal, Kate Saenko, Asli Celikyilmaz, Vikas Chandra:
An Introduction to Vision-Language Modeling. CoRR abs/2405.17247 (2024) - [i39]Sizhe Chen, Arman Zharmagambetov, Saeed Mahloujifar, Kamalika Chaudhuri, Chuan Guo:
Aligning LLMs to Be Robust Against Prompt Injection. CoRR abs/2410.05451 (2024) - 2023
- [c30]Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger, Chuan Guo:
Does Label Differential Privacy Prevent Label Inference Attacks? AISTATS 2023: 4336-4347 - [c29]Elvis Dohmatob, Chuan Guo, Morgane Goibert:
Origins of Low-Dimensional Adversarial Perturbations. AISTATS 2023: 9221-9237 - [c28]Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael G. Rabbat:
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. ICML 2023: 11888-11904 - [c27]Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi:
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano. ICML 2023: 11998-12011 - [c26]Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis. ICML 2023: 15884-15899 - [c25]Kiwan Maeng, Chuan Guo, Sanjay Kariyappa, G. Edward Suh:
Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information. NeurIPS 2023 - [c24]Casey Meehan, Florian Bordes, Pascal Vincent, Kamalika Chaudhuri, Chuan Guo:
Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning. NeurIPS 2023 - [c23]Ruihan Wu, Xiangyu Chen, Chuan Guo, Kilian Q. Weinberger:
Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning. UAI 2023: 2293-2303 - [c22]Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang:
Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter. UAI 2023: 2586-2596 - [i38]Casey Meehan, Florian Bordes, Pascal Vincent, Kamalika Chaudhuri, Chuan Guo:
Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning. CoRR abs/2304.13850 (2023) - [i37]Kiwan Maeng, Chuan Guo, Sanjay Kariyappa, G. Edward Suh:
Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information. CoRR abs/2305.04146 (2023) - [i36]Trishita Tiwari, Suchin Gururangan, Chuan Guo, Weizhe Hua, Sanjay Kariyappa, Udit Gupta, Wenjie Xiong, Kiwan Maeng, Hsien-Hsin S. Lee, G. Edward Suh:
Information Flow Control in Machine Learning through Modular Model Architecture. CoRR abs/2306.03235 (2023) - [i35]Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang:
"Private Prediction Strikes Back!" Private Kernelized Nearest Neighbors with Individual Renyi Filter. CoRR abs/2306.07381 (2023) - [i34]Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
ViP: A Differentially Private Foundation Model for Computer Vision. CoRR abs/2306.08842 (2023) - [i33]Ruihan Wu, Chuan Guo, Kamalika Chaudhuri:
Large-Scale Public Data Improves Differentially Private Image Generation Quality. CoRR abs/2309.00008 (2023) - 2022
- [c21]Amrita Roy Chowdhury, Chuan Guo, Somesh Jha, Laurens van der Maaten:
EIFFeL: Ensuring Integrity for Federated Learning. CCS 2022: 2535-2549 - [c20]Lauren Watson, Chuan Guo, Graham Cormode, Alexandre Sablayrolles:
On the Importance of Difficulty Calibration in Membership Inference Attacks. ICLR 2022 - [c19]Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten:
Bounding Training Data Reconstruction in Private (Deep) Learning. ICML 2022: 8056-8071 - [c18]Awni Y. Hannun, Chuan Guo, Laurens van der Maaten:
Measuring Data Leakage in Machine-Learning Models with Fisher Information (Extended Abstract). IJCAI 2022: 5284-5288 - [c17]Kamalika Chaudhuri, Chuan Guo, Mike Rabbat:
Privacy-aware compression for federated data analysis. UAI 2022: 296-306 - [i32]Antonio Ginart, Laurens van der Maaten, James Zou, Chuan Guo:
Submix: Practical Private Prediction for Large-Scale Language Models. CoRR abs/2201.00971 (2022) - [i31]Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten:
Bounding Training Data Reconstruction in Private (Deep) Learning. CoRR abs/2201.12383 (2022) - [i30]Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger, Chuan Guo:
Does Label Differential Privacy Prevent Label Inference Attacks? CoRR abs/2202.12968 (2022) - [i29]Kamalika Chaudhuri, Chuan Guo, Mike Rabbat:
Privacy-Aware Compression for Federated Data Analysis. CoRR abs/2203.08134 (2022) - [i28]Elvis Dohmatob, Chuan Guo, Morgane Goibert:
Origins of Low-dimensional Adversarial Perturbations. CoRR abs/2203.13779 (2022) - [i27]Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis. CoRR abs/2209.05578 (2022) - [i26]Kiwan Maeng, Chuan Guo, Sanjay Kariyappa, G. Edward Suh:
Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information. CoRR abs/2209.10119 (2022) - [i25]Ruihan Wu, Xiangyu Chen, Chuan Guo, Kilian Q. Weinberger:
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning. CoRR abs/2210.10880 (2022) - [i24]Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi:
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano. CoRR abs/2210.13662 (2022) - [i23]Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Mike Rabbat:
The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning. CoRR abs/2211.03942 (2022) - 2021
- [c16]Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, Douwe Kiela:
Gradient-based Adversarial Attacks against Text Transformers. EMNLP (1) 2021: 5747-5757 - [c15]Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger:
Making Paper Reviewing Robust to Bid Manipulation Attacks. ICML 2021: 11240-11250 - [c14]Yiyou Sun, Chuan Guo, Yixuan Li:
ReAct: Out-of-distribution Detection With Rectified Activations. NeurIPS 2021: 144-157 - [c13]Ruihan Wu, Chuan Guo, Yi Su, Kilian Q. Weinberger:
Online Adaptation to Label Distribution Shift. NeurIPS 2021: 11340-11351 - [c12]Ruihan Wu, Chuan Guo, Awni Y. Hannun, Laurens van der Maaten:
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems. NeurIPS 2021: 11745-11756 - [c11]Weizhe Hua, Yichi Zhang, Chuan Guo, Zhiru Zhang, G. Edward Suh:
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining. NeurIPS 2021: 18527-18538 - [c10]Awni Y. Hannun, Chuan Guo, Laurens van der Maaten:
Measuring data leakage in machine-learning models with Fisher information. UAI 2021: 760-770 - [i22]Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger:
Making Paper Reviewing Robust to Bid Manipulation Attacks. CoRR abs/2102.06020 (2021) - [i21]Awni Y. Hannun, Chuan Guo, Laurens van der Maaten:
Measuring Data Leakage in Machine-Learning Models with Fisher Information. CoRR abs/2102.11673 (2021) - [i20]Ruihan Wu, Chuan Guo, Awni Y. Hannun, Laurens van der Maaten:
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems. CoRR abs/2103.11766 (2021) - [i19]Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, Douwe Kiela:
Gradient-based Adversarial Attacks against Text Transformers. CoRR abs/2104.13733 (2021) - [i18]Hanieh Hashemi, Yongqin Wang, Chuan Guo, Murali Annavaram:
Byzantine-Robust and Privacy-Preserving Framework for FedML. CoRR abs/2105.02295 (2021) - [i17]Ruihan Wu, Chuan Guo, Yi Su, Kilian Q. Weinberger:
Online Adaptation to Label Distribution Shift. CoRR abs/2107.04520 (2021) - [i16]Weizhe Hua, Yichi Zhang, Chuan Guo, Zhiru Zhang, G. Edward Suh:
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining. CoRR abs/2109.14707 (2021) - [i15]Lauren Watson, Chuan Guo, Graham Cormode, Alexandre Sablayrolles:
On the Importance of Difficulty Calibration in Membership Inference Attacks. CoRR abs/2111.08440 (2021) - [i14]Yiyou Sun, Chuan Guo, Yixuan Li:
ReAct: Out-of-distribution Detection With Rectified Activations. CoRR abs/2111.12797 (2021) - [i13]Amrita Roy Chowdhury, Chuan Guo, Somesh Jha, Laurens van der Maaten:
EIFFeL: Ensuring Integrity for Federated Learning. CoRR abs/2112.12727 (2021) - 2020
- [b1]Chuan Guo:
Threats and Countermeasures in Machine Learning Applications. Cornell University, USA, 2020 - [c9]Chuan Guo, Tom Goldstein, Awni Y. Hannun, Laurens van der Maaten:
Certified Data Removal from Machine Learning Models. ICML 2020: 3832-3842 - [i12]Chuan Guo, Awni Y. Hannun, Brian Knott, Laurens van der Maaten, Mark Tygert, Ruiyu Zhu:
Secure multiparty computations in floating-point arithmetic. CoRR abs/2001.03192 (2020) - [i11]Chuan Guo, Ruihan Wu, Kilian Q. Weinberger:
TrojanNet: Embedding Hidden Trojan Horse Models in Neural Networks. CoRR abs/2002.10078 (2020)
2010 – 2019
- 2019
- [c8]Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger:
Simple Black-box Adversarial Attacks. ICML 2019: 2484-2493 - [c7]Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger:
A New Defense Against Adversarial Images: Turning a Weakness into a Strength. NeurIPS 2019: 1633-1644 - [c6]Chuan Guo, Ali Mousavi, Xiang Wu, Daniel Niels Holtmann-Rice, Satyen Kale, Sashank J. Reddi, Sanjiv Kumar:
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. NeurIPS 2019: 4944-4954 - [c5]Chuan Guo, Jared S. Frank, Kilian Q. Weinberger:
Low Frequency Adversarial Perturbation. UAI 2019: 1127-1137 - [i10]Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger:
Simple Black-box Adversarial Attacks. CoRR abs/1905.07121 (2019) - [i9]Tao Yu, Shengyuan Hu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger:
A New Defense Against Adversarial Images: Turning a Weakness into a Strength. CoRR abs/1910.07629 (2019) - [i8]Chuan Guo, Tom Goldstein, Awni Y. Hannun, Laurens van der Maaten:
Certified Data Removal from Machine Learning Models. CoRR abs/1911.03030 (2019) - 2018
- [j4]Chuan Guo, Mike Newman:
On the b-chromatic number of cartesian products. Discret. Appl. Math. 239: 82-93 (2018) - [c4]Chuan Guo, Mayank Rana, Moustapha Cissé, Laurens van der Maaten:
Countering Adversarial Images using Input Transformations. ICLR (Poster) 2018 - [i7]Qiantong Xu, Gao Huang, Yang Yuan, Chuan Guo, Yu Sun, Felix Wu, Kilian Q. Weinberger:
An empirical study on evaluation metrics of generative adversarial networks. CoRR abs/1806.07755 (2018) - [i6]Chuan Guo, Jared S. Frank, Kilian Q. Weinberger:
Low Frequency Adversarial Perturbation. CoRR abs/1809.08758 (2018) - 2017
- [j3]Chuan Guo, Douglas R. Stinson:
A tight bound on the size of certain separating hash families. Australas. J Comb. 67: 294-303 (2017) - [c3]Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger B. Grosse:
Discovering and Exploiting Additive Structure for Bayesian Optimization. AISTATS 2017: 1311-1319 - [c2]Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger:
On Calibration of Modern Neural Networks. ICML 2017: 1321-1330 - [i5]Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger:
On Calibration of Modern Neural Networks. CoRR abs/1706.04599 (2017) - [i4]Chuan Guo, Mayank Rana, Moustapha Cissé, Laurens van der Maaten:
Countering Adversarial Images using Input Transformations. CoRR abs/1711.00117 (2017) - 2016
- [j2]Chuan Guo, Jeffrey O. Shallit, Arseny M. Shur:
Palindromic rich words and run-length encodings. Inf. Process. Lett. 116(12): 735-738 (2016) - [c1]Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger:
Supervised Word Mover's Distance. NIPS 2016: 4862-4870 - 2015
- [j1]Chuan Guo, Douglas R. Stinson, Tran van Trung:
On tight bounds for binary frameproof codes. Des. Codes Cryptogr. 77(2-3): 301-319 (2015) - [i3]Chuan Guo, Jeffrey O. Shallit, Arseny M. Shur:
On the Combinatorics of Palindromes and Antipalindromes. CoRR abs/1503.09112 (2015) - [i2]Chuan Guo, Douglas R. Stinson:
A tight bound on the size of certain separating hash families. CoRR abs/1510.00293 (2015) - 2014
- [i1]Chuan Guo, Douglas R. Stinson, Tran van Trung:
On tight bounds for binary frameproof codes. CoRR abs/1406.6920 (2014)
Coauthor Index
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