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Found 8 papers, 2 papers with code
Graphs Generalization under Distribution Shifts
Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the training distribution.
Graph Bayesian Optimization for Multiplex Influence Maximization
In this problem, the seed set is a combination of influential users and information.
Supervised Algorithmic Fairness in Distribution Shifts: A Survey
Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced with changes in data distributions from source to target domains.
Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling
In practice, these dominant pipeline models may be limited in computational efficiency and generalization capacity because of non-parallel inference and context-free discrete label embeddings.
Pursuing Counterfactual Fairness via Sequential Autoencoder Across Domains
This approach effectively separates environmental information and sensitive attributes from the embedded representation of classification features.
Adaptation Speed Analysis for Fairness-aware Causal Models
In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable.
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs
As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning.
Stacked Kernel Network
Similar to DNNs, a SKN is composed of multiple layers of hidden units, but each parameterized by a RKHS function rather than a finite-dimensional vector.
