| 2026 | AAAI | Mapping on a Budget: Optimizing Spatial Data Collection for ML. | Livia Betti, Farooq Sanni, Gnouyaro Sogoyou, Togbe Agbagla, Cullen Molitor, Tamma Carleton, Esther Rolf |
| 2025 | AAAI | SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery. | Konstantin Klemmer, Esther Rolf, Caleb Robinson, Lester Mackey, Marc Ruwurm |
| 2025 | CVPR | Classification Drives Geographic Bias in Street Scene Segmentation. | Rahul Nair, Bhanu Tokas, Gabriel Tseng, Esther Rolf, Hannah Kerner |
| 2025 | ICML | Using Multiple Input Modalities can Improve Data-Efficiency and O.O.D. Generalization for ML with Satellite Imagery. | Arjun Rao, Esther Rolf |
| 2024 | ECAI | Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous Agents. | Lucia Gordon, Esther Rolf, Milind Tambe |
| 2024 | ICLR | Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks. | Marc Ruwurm, Konstantin Klemmer, Esther Rolf, Robin Zbinden, Devis Tuia |
| 2024 | ICML | Position: Mission Critical - Satellite Data is a Distinct Modality in Machine Learning. | Esther Rolf, Konstantin Klemmer, Caleb Robinson, Hannah Kerner |
| 2024 | ICML | Position: Application-Driven Innovation in Machine Learning. | David Rolnick, Aln Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White |
| 2023 | IJCAI | Fairness and Representation in Satellite-Based Poverty Maps: Evidence of Urban-Rural Disparities and Their Impacts on Downstream Policy. | Emily L. Aiken, Esther Rolf, Joshua Blumenstock |
| 2022 | UAI | Resolving label uncertainty with implicit posterior models. | Esther Rolf, Nikolay Malkin, Alexandros Graikos, Ana Jojic, Caleb Robinson, Nebojsa Jojic |
| 2021 | ICML | Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data. | Esther Rolf, Theodora T. Worledge, Benjamin Recht, Michael I. Jordan |
| 2020 | AISTATS | Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information. | Esther Rolf, Michael I. Jordan, Benjamin Recht |
| 2020 | ICML | Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning. | Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Bjrkegren, Moritz Hardt, Joshua Blumenstock |
| 2019 | IJCAI | Delayed Impact of Fair Machine Learning. | Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt |
| 2018 | ICML | Delayed Impact of Fair Machine Learning. | Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt |