“Influence Sketching”: Finding Influential Samples In Large-Scale Regressions
Authors: Mike Wojnowicz, Ben Cruz, Xuan Zhao, Brian Wallace, Matt Wolff, Jay Luan, Caleb Crable | Published: 2016-11-17 | Updated: 2017-03-23
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AdversariaLib: An Open-source Library for the Security Evaluation of Machine Learning Algorithms Under Attack
Authors: Igino Corona, Battista Biggio, Davide Maiorca | Published: 2016-11-15
Practical Secure Aggregation for Federated Learning on User-Held Data
Authors: Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth | Published: 2016-11-14
Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)
Authors: Xiaolin Wu, Xi Zhang | Published: 2016-11-13 | Updated: 2017-05-26
Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods
Authors: Thai Pham, Steven Lee | Published: 2016-11-12 | Updated: 2017-02-25
Towards the Science of Security and Privacy in Machine Learning
Authors: Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, Michael Wellman | Published: 2016-11-11
SoK: Applying Machine Learning in Security – A Survey
Authors: Heju Jiang, Jasvir Nagra, Parvez Ahammad | Published: 2016-11-10
Learning from Untrusted Data
Authors: Moses Charikar, Jacob Steinhardt, Gregory Valiant | Published: 2016-11-07 | Updated: 2017-06-11
Adversarial Machine Learning at Scale
Authors: Alexey Kurakin, Ian Goodfellow, Samy Bengio | Published: 2016-11-04 | Updated: 2017-02-11
PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers
Authors: Wei Xie, Yang Wang, Steven M. Boker, Donald E. Brown | Published: 2016-11-03