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Found 11 papers, 5 papers with code
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
In this work, we theoretically define the concept of expected robustness in the context of attributed graphs and relate it to the classical definition of adversarial robustness in the graph representation learning literature.
A Simple and Yet Fairly Effective Defense for Graph Neural Networks
Successful combinations of our NoisyGNN approach with existing defense techniques demonstrate even further improved adversarial defense results.
Example-Based Explanations of Random Forest Predictions
A random forest prediction can be computed by the scalar product of the labels of the training examples and a set of weights that are determined by the leafs of the forest into which the test object falls; each prediction can hence be explained exactly by the set of training examples for which the weights are non-zero.
Can local explanation techniques explain linear additive models?
Local model-agnostic additive explanation techniques decompose the predicted output of a black-box model into additive feature importance scores.
Approximating Score-based Explanation Techniques Using Conformal Regression
The results also suggest that the proposed method can produce tight intervals, while providing validity guarantees.
Interpretable Graph Neural Networks for Tabular Data
Data in tabular format is frequently occurring in real-world applications.
Image Keypoint Matching using Graph Neural Networks
Image matching is a key component of many tasks in computer vision and its main objective is to find correspondences between features extracted from different natural images.
Towards interpretability of Mixtures of Hidden Markov Models
Mixtures of Hidden Markov Models (MHMMs) are frequently used for clustering of sequential data.
A study of data and label shift in the LIME framework
LIME is a popular approach for explaining a black-box prediction through an interpretable model that is trained on instances in the vicinity of the predicted instance.
Block-distributed Gradient Boosted Trees
As a result, we are able to reduce the training time for high-dimensional data, and allow more cost-effective scale-out without the need for expensive network communication.
Clustering with Confidence: Finding Clusters with Statistical Guarantees
In this study, we propose a technique for quantifying the instability of a clustering solution and for finding robust clusters, termed core clusters, which correspond to clusters where the co-occurrence probability of each data item within a cluster is at least $1 - \alpha$.
