Publications

Google Scholar, arXiv
ML=Machine Learning, BI=Biological Imaging
* denotes equal contribution

Preprints and Working Papers

[ML] ImageNot: A contrast with ImageNet preserves model rankings
Olawale Salaudeen, Moritz Hardt.
In Review, 2024.
[arXiv] [code]

[ML] Ensemble-Learning for Counterfactual Estimation
Pablo Robles-Granda, Evan D. Anderson, Olawale Salaudeen, Ethan Trewhitt, Christopher E. Zwilling, Elizabeth Whitaker, Aron K. Barbey, Oluwasanmi Koyejo
In Review.

[ML] Towards Accurate Benchmarking of Domain Generalization
Olawale Salaudeen, Oluwasanmi Koyejo.
In Preparation (preprint imminent).

[ML/BI] Causal-ICA-AROMA – Motion Denoising in fMRI via Causal Graphical Model Augmentation of ICA-AROMA”
Olawale Salaudeen et al.
In Preparation (preprint imminent).

[ML] Target Conditioned Representation Independence (TCRI); From Domain-Invariant to Domain-General Representations
Olawale Salaudeen, Oluwasanmi Koyejo.
arXiv, 2022.
[arXiv]

Peer Reviewed

[ML] Causally-Inspired Regularization Enables Domain General Representations
Olawale Salaudeen, Oluwasanmi Koyejo.
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2024. (to appear)

[ML] Proxy Methods for Domain Adaptation
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. (to appear)
[arXiv]

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

[ML] Adapting to Latent Subgroup Shifts via Concepts and Proxies
*Ibrahim Alabdulmohsin, *Nicole Chiou, *Alexander D’Amour, *Arthur Gretton, *Sanmi Koyejo, *Matt J. Kusner, *Stephen R. Pfohl, *Olawale Salaudeen, *Jessica Schrouff, *Katherine Tsai.
Authors listed in alphabetical order
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
[paper]

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

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

[BI] Ultra-fast 3D fMRI to explore cardiac-induced fluctuations in BOLD-based functional imaging
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)
[link]

[ML] Exploiting Causal Chains for Domain Generalization
Olawale Salaudeen, Oluwasanmi Koyejo.
Conference on Neural Information Processing Systems (NeurIPS), 2021. Workshop on Distribution Shifts (DistShift)
[paper] [poster]