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Model-Targeted Poisoning Attacks: Provable Convergence and Certified Bounds

Jun 30, 2020
Fnu Suya, Saeed Mahloujifar, David Evans, Yuan Tian

* 21 pages, code available at: https://github.com/suyeecav/model-targeted-poisoning 

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Revisiting Membership Inference Under Realistic Assumptions

Jun 21, 2020
Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu


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Pointwise Paraphrase Appraisal is Potentially Problematic

Jun 05, 2020
Hannah Chen, Yangfeng Ji, David Evans

* ACL 2020 Student Research Workshop 

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Certifying Joint Adversarial Robustness for Model Ensembles

Apr 21, 2020
Mainuddin Ahmad Jonas, David Evans

* Open source code for our implementation and for reproducing our experiments is available at https://github.com/jonas-maj/ensemble-adversarial-robustness 

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One Neuron to Fool Them All

Mar 20, 2020
Anshuman Suri, David Evans


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Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models

Mar 01, 2020
Xiao Zhang, Jinghui Chen, Quanquan Gu, David Evans

* 14 pages, 2 figures, 5 tables, AISTATS final paper reformatted for readability 

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Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization

Feb 26, 2020
Sicheng Zhu, Xiao Zhang, David Evans

* 18 pages, 6 figures 

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Advances and Open Problems in Federated Learning

Dec 10, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao


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Efficient Privacy-Preserving Nonconvex Optimization

Oct 30, 2019
Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu

* 26 pages, 3 figures, 5 tables 

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Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness

May 29, 2019
Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody, David Evans

* 17 pages, 3 figures, 4 tables 

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When Relaxations Go Bad: "Differentially-Private" Machine Learning

Mar 01, 2019
Bargav Jayaraman, David Evans


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Context-aware Monitoring in Robotic Surgery

Jan 28, 2019
Mohammad Samin Yasar, David Evans, Homa Alemzadeh

* 7 pages, 7 figures, accepted in ISMR2019 

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Cost-Sensitive Robustness against Adversarial Examples

Oct 22, 2018
Xiao Zhang, David Evans

* 16 pages, 5 figures, 3 tables 

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Smoothing and Mapping using Multiple Robots

May 06, 2018
Karthik Paga, Joe Phaneuf, Adam Driscoll, David Evans

* 7 pages, 12 figures 

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Query-limited Black-box Attacks to Classifiers

Dec 23, 2017
Fnu Suya, Yuan Tian, David Evans, Paolo Papotti

* 5 Pages, 2017 NIPS workshop on machine learning and computer security (12/08/2017-12/09/2017) 

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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

Dec 05, 2017
Weilin Xu, David Evans, Yanjun Qi

* To appear in Network and Distributed Systems Security Symposium (NDSS) 2018 

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Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples

May 30, 2017
Weilin Xu, David Evans, Yanjun Qi


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