494 citations · 684 across the 9 of their papers we have counts for
6 papers · 1 filter
Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification
James Atwood, Tina Tian, Ben Packer +5
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…
Detecting Underspecification with Local Ensembles
David Madras, James Atwood, Alex D'Amour
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 pr…
BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity
Alexey A. Gritsenko, Alex D'Amour, James Atwood +2
We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers. The patches encourage internal m…
Sparse Diffusion-Convolutional Neural Networks
James Atwood, Siddharth Pal, Don Towsley +1
The predictive power and overall computational efficiency of Diffusion-convolutional neural networks make them an attractive choice for node classification tasks. However, a naive…
Diffusion-Convolutional Neural Networks
James Atwood, Don Towsley
We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffus…
Learning to Generate Networks
James Atwood, Don Towsley, Krista Gile +1
We investigate the problem of learning to generate complex networks from data. Specifically, we consider whether deep belief networks, dependency networks, and members of the expon…