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Found 7 papers, 1 papers with code
A Survey on Evaluation of Out-of-Distribution Generalization
In closing, we propose several promising directions for future research in OOD evaluation.
Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies
Causal emergence theory aims to bridge these two concepts and even employs measures of causality to quantify emergence.
Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications
Machine learning algorithms minimizing average risk are susceptible to distributional shifts.
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method
The architecture of GTAGC encompasses graph embedding, integration of the Graph Transformer within the autoencoder structure, and a clustering component.
Predictive Heterogeneity: Measures and Applications
As an intrinsic and fundamental property of big data, data heterogeneity exists in a variety of real-world applications, such as precision medicine, autonomous driving, financial applications, etc.
Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling
To better model the diversity of interactions, STGNN introduces a novel aggregation mechanism to organize the most significant historical neighbors' information and adaptively obtain the significance of node pairs.
Enhancing Distributional Stability among Sub-populations
shifts in prediction mechanisms ($Y|X$-shifts).
