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Identification of Attack-Specific Signatures in Adversarial Examples


Oct 13, 2021
Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau, Micah Goldblum, Rama Chellappa


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Stochastic Training is Not Necessary for Generalization


Sep 29, 2021
Jonas Geiping, Micah Goldblum, Phillip E. Pope, Michael Moeller, Tom Goldstein

* 20 pages, 4 figures. Code published at github.com/JonasGeiping/fullbatchtraining 

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Towards Transferable Adversarial Attacks on Vision Transformers


Sep 18, 2021
Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang


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Datasets for Studying Generalization from Easy to Hard Examples


Aug 13, 2021
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein


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Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability


Aug 03, 2021
Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein


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Adversarial Examples Make Strong Poisons


Jun 21, 2021
Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojtek Czaja, Tom Goldstein


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MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data


Jun 17, 2021
Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein


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Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch


Jun 16, 2021
Hossein Souri, Micah Goldblum, Liam Fowl, Rama Chellappa, Tom Goldstein


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Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks


Jun 08, 2021
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein


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SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training


Jun 02, 2021
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein


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The Intrinsic Dimension of Images and Its Impact on Learning


Apr 18, 2021
Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein

* To appear at ICLR 2021 (spotlight), 17 pages with appendix, 15 figures 

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Thinking Deeply with Recurrence: Generalizing from Easy to Hard Sequential Reasoning Problems


Mar 17, 2021
Avi Schwarzschild, Arjun Gupta, Micah Goldblum, Tom Goldstein


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Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release


Mar 05, 2021
Liam Fowl, Ping-yeh Chiang, Micah Goldblum, Jonas Geiping, Arpit Bansal, Wojtek Czaja, Tom Goldstein


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DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations


Mar 02, 2021
Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein

* 11 pages, 5 figures 

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What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors


Feb 26, 2021
Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, Tom Goldstein

* 17 pages, 14 figures 

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Technical Challenges for Training Fair Neural Networks


Feb 12, 2021
Valeriia Cherepanova, Vedant Nanda, Micah Goldblum, John P. Dickerson, Tom Goldstein


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LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition


Jan 25, 2021
Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John Dickerson, Gavin Taylor, Tom Goldstein

* Published as a conference paper at ICLR 2021 

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Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses


Dec 30, 2020
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein


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Data Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses


Dec 18, 2020
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein


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Analyzing the Machine Learning Conference Review Process


Nov 26, 2020
David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, Tom Goldstein

* NeurIPS Workshop on Navigating the Broader Impacts of AI Research. Full version at arXiv:2010.05137 

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Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff


Nov 18, 2020
Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, Arjun Gupta

* Authors ordered alphabetically 

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An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process


Oct 26, 2020
David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, Tom Goldstein

* 19 pages, 6 Figures 

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Data Augmentation for Meta-Learning


Oct 14, 2020
Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein


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Random Network Distillation as a Diversity Metric for Both Image and Text Generation


Oct 13, 2020
Liam Fowl, Micah Goldblum, Arjun Gupta, Amr Sharaf, Tom Goldstein


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Prepare for the Worst: Generalizing across Domain Shifts with Adversarial Batch Normalization


Sep 21, 2020
Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein


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