40 citations · 91 across the 18 of their papers we have counts for
19 papers
Planetary Geospatial Foundation Models: A New Paradigm for Global Public Health
Arbaaz Muslim, Aviv Slobodkin, Katherine Wheeler-Martin +35
The efficacy of traditional disease prediction is limited by spatial gaps and temporal lags, which impact the timing and targets of resource deployments. Outbreaks escalate undetec…
Auditing Predictive Models for Intersectional Biases
Kate S. Boxer, Edward McFowland, Daniel B. Neill
Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for i…
Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness
Neil Menghani, Edward McFowland, Daniel B. Neill
In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic…
Provable Detection of Propagating Sampling Bias in Prediction Models
Pavan Ravishankar, Qingyu Mo, Edward McFowland +1
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipel…
Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs
Chunpai Wang, Daniel B. Neill, Feng Chen
We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs. Scan statistics…
Positional Encoder Graph Neural Networks for Geographic Data
Konstantin Klemmer, Nathan Safir, Daniel B. Neill
Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data. However, they often rely on Euclidean distances to construct the input g…