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Found 6 papers, 1 papers with code
Generating Textual Adversaries with Minimal Perturbation
Many word-level adversarial attack approaches for textual data have been proposed in recent studies.
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations
Given a sequence that is detected as anomalous, we can consider anomalous entry detection as an interpretable machine learning task because identifying anomalous entries in the sequence is to provide an interpretation to the detection result.
Fairness-aware Agnostic Federated Learning
In this paper, we develop a fairness-aware agnostic federated learning framework (AgnosticFair) to deal with the challenge of unknown testing distribution.
Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy
In this work, we analyze the inequality in utility loss by differential privacy and propose a modified differentially private stochastic gradient descent (DPSGD), called DPSGD-F, to remove the potential disparate impact of differential privacy on the protected group.
Achieving Differential Privacy in Vertically Partitioned Multiparty Learning
Evaluation on real-world and synthetic datasets for linear and logistic regressions shows the effectiveness of our proposed method.
FairGAN: Fairness-aware Generative Adversarial Networks
In this paper, we focus on fair data generation that ensures the generated data is discrimination free.
