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Creators/Authors contains: "Koyejo, Sanmi"

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  1. ; ; ; ; ; ; ; ; ; ; et al (, Science)
    Policy must be informed by, but also facilitate the generation of, scientific evidence 
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    Free, publicly-accessible full text available July 31, 2026
  2. ; ; ; ; ; ; ; ; ; ; et al (, Energy and AI)
    Free, publicly-accessible full text available May 1, 2026
  3. ; ; ; ; ; ; ; ; (, International journal of studies on art and humanities)
    Free, publicly-accessible full text available March 24, 2026
  4. ; ; ; ; ; (, Foundations and Trends® in Privacy and Security)
    Free, publicly-accessible full text available January 1, 2026
  5. ; ; ; ; (, Curran Associates, Inc. Advances in Neural Information Processing Systems)
    Globerson, A; Mackey, L; Belgrave, D; Fan, A; Paquet, U; Tomczak, J; Zhang, C (Ed.)
    Last-layer retraining methods have emerged as an efficient framework for correcting existing base models. Within this framework, several methods have been proposed to deal with correcting models for subgroup fairness with and without group membership information. Importantly, prior work has demonstrated that many methods are susceptible to noisy labels. To this end, we propose a drop-in correction for label noise in last-layer retraining, and demonstrate that it achieves state-ofthe-art worst-group accuracy for a broad range of symmetric label noise and across a wide variety of datasets exhibiting spurious correlations. Our proposed approach uses label spreading on a latent nearest neighbors graph and has minimal computational overhead compared to existing methods. 
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    Free, publicly-accessible full text available December 1, 2025
  6. ; ; ; ; ; (, Transactions on machine learning research)
    NA (Ed.)
    Existing methods for last layer retraining that aim to optimize worst-group accuracy (WGA) rely heavily on well-annotated groups in the training data. We show, both in theory and practice, that annotation-based data augmentations using either downsampling or upweighting for WGA are susceptible to domain annotation noise. The WGA gap is exacerbated in highnoise regimes for models trained with vanilla empirical risk minimization (ERM). To this end, we introduce Regularized Annotation of Domains (RAD) to train robust last layer classifiers without needing explicit domain annotations. Our results show that RAD is competitive with other recently proposed domain annotation-free techniques. Most importantly, RAD outperforms state-of-the-art annotation-reliant methods even with only 5% noise in the training data for several publicly available datasets. 
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    Free, publicly-accessible full text available December 1, 2025
  7. ; ; (, PLOS Global Public Health)
    Robinson, Julia (Ed.)
    Full Text Available 
  8. ; ; ; ; ; ; ; ; ; ; et al (, Nature Machine Intelligence)
    Free, publicly-accessible full text available February 1, 2026
  9. ; (, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; ; ; ; ; (, ACM)
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