Publications

You can find a more complete set of my papers on my Google Scholar profile.

Core Areas. Robust Machine/Deep Learning, Biological Imaging
Topics. Distribution Shift, Causality, AI Benchmarking, Algorithmic Fairness, Denoising fMRI
Applications Areas. Healthcare, AI Governance, Neuroscience

Selected

Adapting to Latent Subgroup Shifts via Concepts and Proxies [PDF] [Code]
Ibrahim Alabdulmohsin*, Nicole Chiou*, Alexander D'Amour*, Arthur Gretton*, Sanmi Koyejo*, Matt J. Kusner*, Stephen R. Pfohl*, Olawale Salaudeen*, Jessica Schrouff*, Katherine Tsai*
* denotes equal contribution
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Machine Learning Distribution Shift Causality

Causally-Inspired Regularization Enables Domain General Representations [PDF] [Code]
Olawale Salaudeen, Oluwasanmi Koyejo
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Machine Learning Distribution Shift Causality

On Domain Generalization Datasets as Proxy Benchmarks for Causal Representation Learning (Oral Presentation) [PDF]
Olawale Salaudeen, Nicole Chiou, Sanmi Koyejo
Workshop on Causal Representation Learning, Conference on Neural Information Processing Systems (NeurIPS), 2024
Machine Learning Benchmarking Distribution Shift

ImageNot: A contrast with ImageNet preserves model rankings [PDF] [Code]
Olawale Salaudeen, Moritz Hardt
arXiv
Machine Learning Benchmarking Distribution Shift

All

On Domain Generalization Datasets as Proxy Benchmarks for Causal Representation Learning (Oral Presentation) [PDF]
Olawale Salaudeen, Nicole Chiou, Sanmi Koyejo
Workshop on Causal Representation Learning, Conference on Neural Information Processing Systems (NeurIPS), 2024
Machine Learning Benchmarking Distribution Shift

fMRI Motion Denoising Reimagined: ICA-AROMA meets Causality
Olawale Salaudeen, Paul Camacho, Aron Barbey, Brad Sutton, Sanmi Koyejo
In Review
Biological Imaging Causality

Proxy Methods for Domain Adaptation [PDF] [Code]
Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Machine Learning Distribution Shift Causality

Causally-Inspired Regularization Enables Domain General Representations [PDF] [Code]
Olawale Salaudeen, Oluwasanmi Koyejo
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Machine Learning Distribution Shift Causality

ImageNot: A contrast with ImageNet preserves model rankings [PDF] [Code]
Olawale Salaudeen, Moritz Hardt
arXiv
Machine Learning Benchmarking Distribution Shift

Adapting to Latent Subgroup Shifts via Concepts and Proxies [PDF] [Code]
Ibrahim Alabdulmohsin*, Nicole Chiou*, Alexander D'Amour*, Arthur Gretton*, Sanmi Koyejo*, Matt J. Kusner*, Stephen R. Pfohl*, Olawale Salaudeen*, Jessica Schrouff*, Katherine Tsai*
* denotes equal contribution
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Machine Learning Distribution Shift Causality

Understanding subgroup performance differences of fair predictors using causal models [Link]
Stephen Robert Pfohl, Natalie Harris, Chirag Nagpal, David Madras, Vishwali Mhasawade, Olawale Elijah Salaudeen, Katherine A Heller, Sanmi Koyejo, Alexander Nicholas D'Amour
Workshop on Distribution Shifts (DistShift), Conference on Neural Information Processing Systems (NeurIPS), 2023.
Machine Learning Algorithmic Fairness Causality

Adapting to Shifts in Latent Confounders using Observed Concepts and Proxies_ [PDF]
Matt J. Kusner, Ibrahim Alabdulmohsin, Stephen Pfohl, Olawale Salaudeen, Arthur Gretton, Sanmi Koyejo, Jessica Schrouff, Alexander D’Amour
Workshop on Principles of Distribution Shift (PODS), International Conference on Machine Learning (ICML), 2022.
Machine Learning Distribution Shift Causality

Addressing Observational Biases in Algorithmic Fairness Assessments [Poster]
Chirag Nagpal, Olawale Salaudeen, Sanmi Koyejo, Stephen Pfohl
Workshop on Algorithmic Fairness through the Lens of Causality and Privacy, Conference on Neural Information Processing Systems (NeurIPS), 2022 (extended abstract).
Machine Learning Algorithmic Fairness

Ultra-fast 3D fMRI to explore cardiac-induced fluctuations in BOLD-based functional imaging [PDF]
Brad Sutton, Aaron Anderson, Benjamin Zimmerman, Paul Camacho, Riwei Jin, Charles Marchini, Olawale Salaudeen, Natalie Ramsy, Davide Boido, Serge Charpak, Andrew Webb, Luisa Ciobanu
International Society for Magnetic Resonance in Medicine (ISMRM), 2022 (abstract)
Biological Imaging

Exploiting Causal Chains for Domain Generalization [PDF]
Olawale Salaudeen, Oluwasanmi Koyejo
Workshop on Distribution Shifts (DistShift), Conference on Neural Information Processing Systems (NeurIPS), 2021.
Machine Learning Distribution Shift Causality