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Found 5 papers, 2 papers with code
Explanations of Black-Box Models based on Directional Feature Interactions
As machine learning algorithms are deployed ubiquitously to a variety of domains, it is imperative to make these often black-box models transparent.
Geometry of Score Based Generative Models
We demonstrate that looking from geometric perspective enables us to answer many of these questions and provide new interpretations to some known results.
Divide and Compose with Score Based Generative Models
Towards the direction of having more control over image manipulation and conditional generation, we propose to learn image components in an unsupervised manner so that we can compose those components to generate and manipulate images in informed manner.
Boundary-Aware Uncertainty for Feature Attribution Explainers
In this work we propose the Gaussian Process Explanation UnCertainty (GPEC) framework, which generates a unified uncertainty estimate combining decision boundary-aware uncertainty with explanation function approximation uncertainty.
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
We provide lower bound guarantees on the astuteness of a variety of explainers (e. g., SHAP, RISE, CXPlain) given the Lipschitzness of the prediction function.
