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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 61–70 of 135 papers

TitleStatusHype
Selective Transfer Machine for Personalized Facial Action Unit Detection—0
Self-Training: A Survey—0
Semi-Supervised Prediction of Gene Regulatory Networks Using Machine Learning Algorithms—0
Sharp Generalization of Transductive Learning: A Transductive Local Rademacher Complexity Approach—0
Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates—0
Smoothed Analysis of Sequential Probability Assignment—0
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time—0
The Benefits and Risks of Transductive Approaches for AI Fairness—0
Towards Adversarial Robustness via Transductive Learning—0
Towards Understanding the Generalization of Graph Neural Networks—0
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