2 citations · 4 across the 6 of their papers we have counts for
6 papers
Beyond Point Predictions: Uncertainty-Aware Satellite Poverty Mapping for Public Policy
Markus B. Pettersson, James Bailie, Mohammad Kakooei +2
Despite their critical importance for policy and research, high-resolution poverty data remain limited across much of Africa. Machine learning (ML) with earth observation (EO) imag…
Performative Learning Theory
Julian Rodemann, Unai Fischer-Abaigar, James Bailie +1
Performative predictions influence the very outcomes they aim to forecast. We study performative predictions that affect a sample (e.g., only existing users of an app) and/or the w…
Why Data Anonymization Has Not Taken Off
Matthew J. Schneider, James Bailie, Dawn Iacobucci
Companies are looking to data anonymization research $\unicode{x2013}$ including differential private and synthetic data methods $\unicode{x2013}$ for simple and straightforward co…
Generalization Bounds and Stopping Rules for Learning with Self-Selected Data
Julian Rodemann, James Bailie
Many learning paradigms self-select training data in light of previously learned parameters. Examples include active learning, semi-supervised learning, bandits, or boosting. Rodem…
A High Resolution Urban and Rural Settlement Map of Africa Using Deep Learning and Satellite Imagery
Mohammad Kakooei, James Bailie, Markus B. Pettersson +3
Accurate and consistent mapping of urban and rural areas is crucial for sustainable development, spatial planning, and policy design. It is particularly important in simulating the…
The Complexities of Differential Privacy for Survey Data
Jörg Drechsler, James Bailie
The concept of differential privacy (DP) has gained substantial attention in recent years, most notably since the U.S. Census Bureau announced the adoption of the concept for its 2…