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Transductive Learning

In this setting, both a labeled training sample and an (unlabeled) test sample are provided at training time. The goal is to predict only the labels of the given test instances as accurately as possible.

Papers

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Showing 1–10 of 135 papers

TitleStatusHype
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction—0
Few-shot Novel Category DiscoveryCode0
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a MeasurementCode1
Accurate and Scalable Graph Neural Networks via Message InvarianceCode0
Generate, Transduct, Adapt: Iterative Transduction with VLMs—0
Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks—0
Single-View Graph Contrastive Learning with Soft Neighborhood AwarenessCode0
Predictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media DiscourseCode0
A Theory for Compressibility of Graph Transformers for Transductive Learning—0
UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language ModelsCode0
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