To overcome these limitations, we propose a ZSAR framework which does not rely on nearest neighbor classification, but rather consists of a pairwise scoring function.

Recent work has shown that a simple, fast method called Simple Graph Convolution (SGC) (Wu et al., 2019), which eschews deep learning, is competitive with

Recent works have performed this matching in the original feature space thus requiring predefined distance functions.

Here we test the sensitivity of model w.r.t. the hyperparameter Figure 1 shows the change of fairness (equalized odds) under different cutoff value. We show the effect of validation size on accuracy and equalized odds in Fig. . Figure 4: Change of accuracy as validation size varies. For ease of exposition, here we consider binary classification (i.e., Figure 5: Change of equalized odds as validation size varies. In Section A.1 above, we showed the sensitivity of model performance and fairness

Fairness has become an important topic in machine learning.

We identify the information-theoretic threshold below which obtaining a positive correlation with the true cluster means is statistically impossible.

Bayesian optimization (BO) is a powerful approach for optimizing black-box, expensive-to-evaluate functions. To enable a flexible trade-off between the cost and accuracy, many applications allow the function to be evaluated at different fidelities.

Estimating information from structured data is a central theme in statistics that by now has found applications in a wide array of disciplines.

In this paper, we also apply inference to the prior parameters of Gaussian BNNs (e.g.

Despite the importance of graph OD and many algorithms being developed for it in recent years, there is no comprehensive benchmark on graph outlier detection, which we believe has hindered the development and understanding of graph OD algorithms.