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Bayesian Learning with Information Gain Provably Bounds Risk for a
Robust Adversarial Defense

v1v2 (latest)

Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense

International Conference on Machine Learning (ICML), 2022

5 December 2022

Ehsan Abbasnejad

Javen Qinfeng Shi

Damith Ranashinghe

ArXiv (abs)PDF HTML Github

Papers citing "Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense"

5 / 5 papers shown

Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble
of Teachers

Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble of Teachers

Shayan Mohajer Hamidi

308

3

0

22 May 2024

Attacking Bayes: On the Adversarial Robustness of Bayesian Neural
Networks

Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks

Tim G. J. Rudner

Nikolaos Tsilivis

405

3

0

27 Apr 2024

Bayesian Learned Models Can Detect Adversarial Malware For Free

Bayesian Learned Models Can Detect Adversarial Malware For Free

Dang Quang Nguyen

Ehsan Abbasnejad

Damith C. Ranasinghe

236

2

0

27 Mar 2024

Post-train Black-box Defense via Bayesian Boundary Correction

Post-train Black-box Defense via Bayesian Boundary Correction

Yunfeng Diao

545

1

0

29 Jun 2023

Feature-Space Bayesian Adversarial Learning Improved Malware Detector
Robustness

Feature-Space Bayesian Adversarial Learning Improved Malware Detector RobustnessAAAI Conference on Artificial Intelligence (AAAI), 2023

Ehsan Abbasnejad

Damith C. Ranasinghe

267

10

0

30 Jan 2023

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