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Found 4 papers, 2 papers with code
Benchmarking Algorithms for Federated Domain Generalization
We then apply our methodology to evaluate 14 Federated DG methods, which include centralized DG methods adapted to the FL context, FL methods that handle client heterogeneity, and methods designed specifically for Federated DG.
Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models
Answering counterfactual queries has important applications such as explainability, robustness, and fairness but is challenging when the causal variables are unobserved and the observations are non-linear mixtures of these latent variables, such as pixels in images.
Resilience to Multiple Attacks via Adversarially Trained MIMO Ensembles
We show that ensemble methods can improve adversarial robustness to multiple attacks if the ensemble is \emph{adversarially diverse}, which is defined by two properties: 1) the sub-models are adversarially robust themselves and yet 2) adversarial attacks do not transfer easily between sub-models.
Exploring Adversarial Examples via Invertible Neural Networks
We propose a new way of achieving such understanding through a recent development, namely, invertible neural models with Lipschitz continuous mapping functions from the input to the output.
