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Found 8 papers, 2 papers with code
Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification
Despite the rich literature on machine learning fairness, relatively little attention has been paid to remediating complex systems, where the final prediction is the combination of multiple classifiers and where multiple groups are present.
Fair treatment allocations in social networks
Simulations of infectious disease spread have long been used to understand how epidemics evolve and how to effectively treat them.
Detecting Underspecification with Local Ensembles
We present local ensembles, a method for detecting underspecification -- when many possible predictors are consistent with the training data and model class -- at test time in a pre-trained model.
BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity
We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers.
No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World
Further, we analyze classifiers trained on these data sets to assess the impact of these training distributions and find strong differences in the relative performance on images from different locales.
Sparse Diffusion-Convolutional Neural Networks
The predictive power and overall computational efficiency of Diffusion-convolutional neural networks make them an attractive choice for node classification tasks.
Diffusion-Convolutional Neural Networks
We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data.
Ranked #7 on
Node Classification
on PubMed (0.1%)
Learning to Generate Networks
We investigate the problem of learning to generate complex networks from data.
