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Found 11 papers, 4 papers with code
Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift
Invariant models are also supposed to generalize to shifts in the marginal distribution $p(X_{\text{inv}})$ of the extracted features $X_{\text{inv}}$, a type of shift we call an $\textit{invariant covariate shift}$.
Novel Node Category Detection Under Subpopulation Shift
We introduce a new approach, Recall-Constrained Optimization with Selective Link Prediction (RECO-SLIP), to detect nodes belonging to novel categories in attributed graphs under subpopulation shifts.
Data Augmentations for Improved (Large) Language Model Generalization
The reliance of text classifiers on spurious correlations can lead to poor generalization at deployment, raising concerns about their use in safety-critical domains such as healthcare.
Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy
However, even when the stable feature determines the label in the training distribution and the shortcut does not provide any additional information, like in perception tasks, default-ERM still exhibits shortcut learning.
Malign Overfitting: Interpolation Can Provably Preclude Invariance
This suggests that the phenomenon of "benign overfitting", in which models generalize well despite interpolating, might not favorably extend to settings in which robustness or fairness are desirable.
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation
In prediction tasks, there exist features that are related to the label in the same way across different settings for that task; these are semantic features or semantics.
In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Accurately predicting the relevance of items to users is crucial to the success of many social platforms.
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
A natural source for such attributes is the StyleSpace of StyleGAN, which is known to generate semantically meaningful dimensions in the image.
On Calibration and Out-of-domain Generalization
In this work, we draw a link between OOD performance and model calibration, arguing that calibration across multiple domains can be viewed as a special case of an invariant representation leading to better OOD generalization.
Globally Optimal Learning for Structured Elliptical Losses
The core of the difficulty is the non-convexity of the objective function, implying that standard optimization algorithms may converge to sub-optimal critical points.
