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stat.ME2022
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml +1
We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment a…
cs.LG2022
Provable concept learning for interpretable predictions using variational autoencoders
Armeen Taeb, Nicolo Ruggeri, Carina Schnuck +1
In safety-critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available. Many attempts to provide such explanations r…