arXiv Machine Learning
Rank-Constrained Adaptation for Reliable Real-World Performance
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arXiv Machine Learning
Sep 2
SAGE: Subpopulation-Aware Generative Enhancement for Mitigating Spurious Correlations
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.
arXiv AI
2d ago
Optimal Transport Reweighting for Robust Learning under Spurious Correlations and Label Noise
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...
arXiv AI
Sep 10
Discovering Latent Groups for Robust Classification
arXiv:2606.23609v2 Announce Type: replace-cross Abstract: Machine learning models exploit spurious correlations, achieving high average accuracy but failing disproportionately on underrepresented sub...
arXiv Machine Learning
Aug 14
ProME: Prototype-Margin Environments with Repair-Aware Selection for Group-Robust Learning
arXiv:2608. 13190v1 Announce Type: new Abstract: Group-robust learning is crucial for maintaining accuracy on rare subpopulations when training-group labels are unavailable.
Hugging Face Trending Papers
Jun 22
Discovering Latent Groups for Robust Classification
Machine learning models exploit spurious correlations, achieving high average accuracy but failing disproportionately on underrepresented subgroups. Existing methods address this by adjusting network parameters, guided either by subgroup annotations or inferred pseudo-group labels.
arXiv AI
Sep 10
DUA-D2C: Dynamic Uncertainty Aware Method for Overfitting Remediation in Deep Learning
The paper introduces DUA-D2C, a Dynamic Uncertainty-Aware Divide2Conquer method that improves overfitting remediation in deep learning. It refines the traditional Divide2Conquer approach by dynamically weighting subset models based on a composite score of accuracy and normalized prediction entropy, allowing the central model to learn more from generalizable and confident edge models. The authors provide theoretical justification, show reduced model variance, and demonstrate significant generalization gains across image, audio, and text benchmarks, even when combined with standard regularizers like Dropout.