NodePFN pre-trains on synthetic graphs with controllable homophily and causal feature-label models to achieve 71.27 average accuracy on 23 node classification benchmarks without graph-specific training.
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RGBT combines GMM-derived instance reliability weights with a Bayes-label transition matrix to achieve consistent, low-variance estimation from noisy implicit feedback while using all samples.
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Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
NodePFN pre-trains on synthetic graphs with controllable homophily and causal feature-label models to achieve 71.27 average accuracy on 23 node classification benchmarks without graph-specific training.
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Robust Recommendation from Noisy Implicit Feedback: A GMM-Weighted Bayes-label Transition Matrix Framework
RGBT combines GMM-derived instance reliability weights with a Bayes-label transition matrix to achieve consistent, low-variance estimation from noisy implicit feedback while using all samples.