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Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery
We frame heteroskedasticity in a structural causal model framework and present an adaptation of the partial correlation CI test that works well in the presence of heteroskedastic noise, given that expert knowledge about the heteroskedastic relationships is available.
Risk Assessment for Machine Learning Models
In this paper we propose a framework for assessing the risk associated with deploying a machine learning model in a specified environment.
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study
Therefore, we study the minimum distance of data points to the decision boundary and how this margin evolves over the training of a deep neural network.
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks
We call this the "attack generator".
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