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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 51–100 of 135 papers

TitleStatusHype
Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning—0
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning—0
Regularization and Optimal Multiclass Learning—0
Relax and Randomize : From Value to Algorithms—0
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives—0
Robust Collective Classification against Structural Attacks—0
Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines—0
Scalable Semi-Supervised Learning over Networks using Nonsmooth Convex Optimization—0
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
Transductive Boltzmann Machines—0
Transductive image segmentation: Self-training and effect of uncertainty estimation—0
Transductive Learning for Abstractive News Summarization—0
Transductive Learning for Abstractive News Summarization—0
Transductive Learning for Multi-Task Copula Processes—0
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections—0
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models—0
Transductive Learning for Zero-Shot Object Detection—0
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis—0
Transductive Learning with Multi-class Volume Approximation—0
Transductive Learning with String Kernels for Cross-Domain Text Classification—0
Transductive Multi-class and Multi-label Zero-shot Learning—0
Transductive Multi-label Zero-shot Learning—0
Transductive Non-linear Learning for Chinese Hypernym Prediction—0
Transductive Rademacher Complexity and its Applications—0
Transductive Semi-Supervised Deep Learning using Min-Max Features—0
Transductive Unbiased Embedding for Zero-Shot Learning—0
Transductive Zero-Shot Learning with a Self-training dictionary approach—0
Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation—0
Understanding Generalization via Leave-One-Out Conditional Mutual Information—0
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
VLSI Hypergraph Partitioning with Deep Learning—0
Bayesian Circular Regression with von Mises Quasi-Processes—0
Without-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization—0
Estimating class separability of text embeddings with persistent homology—0
Without-Replacement Sampling for Stochastic Gradient Methods—0
Active Few-Shot Fine-Tuning—0
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction—0
An Iterative Co-Training Transductive Framework for Zero Shot Learning—0
Anomaly Detection of Tabular Data Using LLMs—0
A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process—0
A Theory for Compressibility of Graph Transformers for Transductive Learning—0
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