Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Authors: Hadi Salman, Greg Yang, Jerry Li, Pengchuan Zhang, Huan Zhang, Ilya Razenshteyn, Sebastien Bubeck | Published: 2019-06-09 | Updated: 2020-01-10
Adversarial Attack Generation Empowered by Min-Max Optimization
Authors: Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, Bo Li | Published: 2019-06-09 | Updated: 2021-11-01
Real or Fake? Learning to Discriminate Machine from Human Generated Text
Authors: Anton Bakhtin, Sam Gross, Myle Ott, Yuntian Deng, Marc'Aurelio Ranzato, Arthur Szlam | Published: 2019-06-07 | Updated: 2019-11-25
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
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