Adversarial Regression with Multiple Learners
Authors: Liang Tong, Sixie Yu, Scott Alfeld, Yevgeniy Vorobeychik | Published: 2018-06-06
Literature Database
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Authors: Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu | Published: 2018-06-06
Killing four birds with one Gaussian process: the relation between different test-time attacks
Authors: Kathrin Grosse, Michael T. Smith, Michael Backes | Published: 2018-06-06 | Updated: 2020-11-29
Set-based Obfuscation for Strong PUFs against Machine Learning Attacks
Authors: Jiliang Zhang, Chaoqun Shen | Published: 2018-06-06 | Updated: 2019-11-13
Evidential Deep Learning to Quantify Classification Uncertainty
Authors: Murat Sensoy, Lance Kaplan, Melih Kandemir | Published: 2018-06-05 | Updated: 2018-10-31
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks
Authors: Chirag Agarwal, Bo Dong, Dan Schonfeld, Anthony Hoogs | Published: 2018-06-05 | Updated: 2018-06-06
PAC-learning in the presence of evasion adversaries
Authors: Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal | Published: 2018-06-05 | Updated: 2018-06-06
Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise
Authors: Vahid Behzadan, Arslan Munir | Published: 2018-06-04
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models
Authors: Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, Michael Backes | Published: 2018-06-04 | Updated: 2018-12-14
Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks
Authors: Yarin Gal, Lewis Smith | Published: 2018-06-02 | Updated: 2018-06-28