arXiv:2606.23609v2 Announce Type: replace-cross
Abstract: Machine learning models exploit spurious correlations, achieving high average accuracy but failing disproportionately on underrepresented sub...
By Ankur Garg, Ulrich A\"ivodji, Samira Ebrahimi Kahou, Vincent Michalski
SAGE (Subpopulation-Aware Generative Enhancement) is a two-stage generative augmentation framework designed to mitigate spurious correlations in machine learning when group labels are unavailable. It uses cluster-derived sub-labels and class labels to fine‑tune a conditional generative model and text encoder, producing synthetic data that fills underrepresented regions and creates a balanced validation set for last‑layer reweighting. Experiments show SAGE improves worst‑group accuracy to 89.5%, 85.7%, and 79.1% on Waterbirds, CelebA, and MetaShift, outperforming existing group‑label‑free baselines by up to 7.7 percentage points.
By Yiming Luo, Rongqiang Zhao, Jie Liu
arXiv:2602.06924v3 Announce Type: replace
Abstract: Deep learning models trained to optimize average accuracy often exhibit systematic failures on particular subpopulations. In real-world settings li...
By Abinitha Gourabathina, Hyewon Jeong, Teya Bergamaschi, Marzyeh Ghassemi, Collin Stultz
arXiv:2610.01028v1 Announce Type: cross
Abstract: Machine learning models often suffer performance degradation under subpopulation shift, particularly when spurious correlations cause models to rely...
By Sung Ho Jo, Seonghwi Kim, Wonsang Yun, Minwoo Chae
arXiv:2502. 18975v2 Announce Type: replace Abstract: Machine learning models are inherently bound to the distribution of the training data, often exploiting non-causal shortcuts.
By Martin Surner, Abdelmajid Khelil, Ludwig Bothmann
arXiv:2608.30297v1 Announce Type: new
Abstract: Attributes describing data content and context can induce diverse imbalance patterns that go beyond label imbalance alone. However, existing studies pr...
By Hanshu Rao, Guangzeng Han, Xiaolei Huang