
Tensor Networks, particularly Matrix Product States (MPS), provide a powerful framework for developing explainable machine learning models that can effectively detect anomalies in cybersecurity data while offering unprecedented transparency into the decision-making process.


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tensor-networks-for-explainable-anomaly-detection-in-cybersecurity


Tensor Networks for Explainable Anomaly Detection in Cybersecurity


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Adversarial random forests (ARF) can be leveraged to efficiently generate plausible counterfactual explanations that are also sparse and proximal to the original instance.


generating-plausible-and-actionable-counterfactual-explanations-with-adversarial-random-forests


Generating Plausible and Actionable Counterfactual Explanations with Adversarial Random Forests
