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Found 9 papers, 4 papers with code
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization
REASON consists of Topological Causal Discovery and Individual Causal Discovery.
Personalized Federated Learning via Heterogeneous Modular Networks
Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention.
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification
Graph Neural Networks (GNNs) are vulnerable to data poisoning attacks, which will generate a poisoned graph as the input to the GNN models.
FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems
Moreover, we observe that open-set face recognition systems are more vulnerable than closed-set systems under different types of attacks.
Towards Robustness against Unsuspicious Adversarial Examples
We then experimentally demonstrate that our attacks indeed do not significantly change perceptual salience of the background, but are highly effective against classifiers robust to conventional attacks.
Defending Against Physically Realizable Attacks on Image Classification
Finally, we demonstrate that adversarial training using our new attack yields image classification models that exhibit high robustness against the physically realizable attacks we study, offering the first effective generic defense against such attacks.
Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning
We then use these in a double-oracle framework to obtain an approximate equilibrium of the game, which in turn yields a robust stochastic policy for the defender.
Adversarial Regression with Multiple Learners
We present an algorithm for computing this equilibrium, and show through extensive experiments that equilibrium models are significantly more robust than conventional regularized linear regression.
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features
A conventional approach to evaluate ML robustness to such attacks, as well as to design robust ML, is by considering simplified feature-space models of attacks, where the attacker changes ML features directly to effect evasion, while minimizing or constraining the magnitude of this change.
