Semisupervised learning uses the trained encoder in learning a predictive model on both labeled and unlabeleddata. Figure 3: The proposed data corruption procedure. Original feature matrix(X) consists of four samples xi,i = 1...,4, where each row/column represents a sample/feature, and the features in each sample are represented by the same color. In the experiment section of the main manuscript, we evaluate VIME and its benchmarks on 11 datasets(6genomics,2clinical,and3publicdatasets). The selected SNPs and the corresponding blood cell trait together form an independent labeled dataset.

Predominately in explainable artificial intelligence (XAI) research, the Shapley value (SV) is applied to determine feature attributions for any black box model. Shapley interaction indices extend the SV to define any-order feature interactions. Defining a unique Shapley interaction index is an open research question and, so far, three definitions have been proposed, which differ by their choice of axioms. Moreover, each definition requires a specific approximation technique. Here, we propose SHAPley Interaction Quantification (SHAP-IQ), an efficient sampling-based approximator to compute Shapley interactions for arbitrary cardinal interaction indices (CII), i.e. interaction indices that satisfy the linearity, symmetry and dummy axiom.

Hierarchical relations are prevalent and indispensable for organizing human knowledge captured by a knowledge graph (KG). The key property of hierarchical relations is that they induce a partial ordering over the entities, which needs to be modeled in order to allow for hierarchical reasoning.