Robustness for Non-Parametric Classification: A Generic Attack and Defense
Authors: Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri | Published: 2019-06-07 | Updated: 2020-02-24
Literature Database
A cryptographic approach to black box adversarial machine learning
Authors: Kevin Shi, Daniel Hsu, Allison Bishop | Published: 2019-06-07 | Updated: 2020-02-21
Computing Tight Differential Privacy Guarantees Using FFT
Authors: Antti Koskela, Joonas Jälkö, Antti Honkela | Published: 2019-06-07 | Updated: 2019-11-04
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness
Authors: Walt Woods, Jack Chen, Christof Teuscher | Published: 2019-06-07 | Updated: 2019-08-06
Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Authors: Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek | Published: 2019-06-06 | Updated: 2019-12-17
Evaluating Explanation Methods for Deep Learning in Security
Authors: Alexander Warnecke, Daniel Arp, Christian Wressnegger, Konrad Rieck | Published: 2019-06-05 | Updated: 2020-04-27
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Authors: Pantelis Elinas, Edwin V. Bonilla, Louis Tiao | Published: 2019-06-05 | Updated: 2020-10-21
Adversarial Training is a Form of Data-dependent Operator Norm Regularization
Authors: Kevin Roth, Yannic Kilcher, Thomas Hofmann | Published: 2019-06-04 | Updated: 2020-10-23
DAWN: Dynamic Adversarial Watermarking of Neural Networks
Authors: Sebastian Szyller, Buse Gul Atli, Samuel Marchal, N. Asokan | Published: 2019-06-03 | Updated: 2021-07-16
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models
Authors: Paweł Morawiecki, Przemysław Spurek, Marek Śmieja, Jacek Tabor | Published: 2019-06-03 | Updated: 2019-07-03