17 citations · 55 across the 12 of their papers we have counts for
11 papers · 1 filter
Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy
Emily Aiken, Esther Rolf, Joshua Blumenstock
Poverty maps derived from satellite imagery are increasingly used to inform high-stakes policy decisions, such as the allocation of humanitarian aid and the distribution of governm…
Evaluation Challenges for Geospatial ML
Esther Rolf
As geospatial machine learning models and maps derived from their predictions are increasingly used for downstream analyses in science and policy, it is imperative to evaluate thei…
Can Strategic Data Collection Improve the Performance of Poverty Prediction Models?
Satej Soman, Emily Aiken, Esther Rolf +1
Machine learning-based estimates of poverty and wealth are increasingly being used to guide the targeting of humanitarian aid and the allocation of social assistance. However, the…
Striving for data-model efficiency: Identifying data externalities on group performance
Esther Rolf, Ben Packer, Alex Beutel +1
Building trustworthy, effective, and responsible machine learning systems hinges on understanding how differences in training data and modeling decisions interact to impact predict…
Resolving label uncertainty with implicit posterior models
Esther Rolf, Nikolay Malkin, Alexandros Graikos +3
We propose a method for jointly inferring labels across a collection of data samples, where each sample consists of an observation and a prior belief about the label. By implicitly…
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data
Esther Rolf, Theodora Worledge, Benjamin Recht +1
Collecting more diverse and representative training data is often touted as a remedy for the disparate performance of machine learning predictors across subpopulations. However, a…
A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery
Esther Rolf, Jonathan Proctor, Tamma Carleton +5
Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-po…
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information
Esther Rolf, Michael I. Jordan, Benjamin Recht
Observational data are often accompanied by natural structural indices, such as time stamps or geographic locations, which are meaningful to prediction tasks but are often discarde…
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Esther Rolf, Max Simchowitz, Sarah Dean +4
While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic polici…
A Successive-Elimination Approach to Adaptive Robotic Sensing
Esther Rolf, David Fridovich-Keil, Max Simchowitz +2
We study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions. Background sign…
Delayed Impact of Fair Machine Learning
Lydia T. Liu, Sarah Dean, Esther Rolf +2
Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventi…