494 citations · 684 across the 9 of their papers we have counts for
9 papers
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…
Fair treatment allocations in social networks
James Atwood, Hansa Srinivasan, Yoni Halpern +1
Simulations of infectious disease spread have long been used to understand how epidemics evolve and how to effectively treat them. However, comparatively little attention has been…
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…
No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World
Shreya Shankar, Yoni Halpern, Eric Breck +3
Modern machine learning systems such as image classifiers rely heavily on large scale data sets for training. Such data sets are costly to create, thus in practice a small number o…
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…
Efficient Network Generation Under General Preferential Attachment
James Atwood, Bruno Ribeiro, Don Towsley
Preferential attachment (PA) models of network structure are widely used due to their explanatory power and conceptual simplicity. PA models are able to account for the scale-free…