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Extreme Multi-Label Classification

Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the unbalanced labels in the dataset and the amount of different labels.

Papers

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Showing 51–75 of 75 papers

TitleStatusHype
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification—0
Exploring space efficiency in a tree-based linear model for extreme multi-label classification—0
Extreme Classification for Answer Type Prediction in Question Answering—0
Extreme Multi-label Classification from Aggregated Labels—0
Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction—0
Extreme Multi-Label Skill Extraction Training using Large Language Models—0
Fine-grained Generalization Analysis of Vector-valued Learning—0
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation—0
HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing—0
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification—0
Label Disentanglement in Partition-based Extreme Multilabel Classification—0
Learning from eXtreme Bandit Feedback—0
Learning label-label correlations in Extreme Multi-label Classification via Label Features—0
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model—0
Locally Non-linear Embeddings for Extreme Multi-label Learning—0
Multi-label Ranking: Mining Multi-label and Label Ranking Data—0
On Data Augmentation for Extreme Multi-label Classification—0
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification—0
On Riemannian Approach for Constrained Optimization Model in Extreme Classification Problems—0
Open Vocabulary Extreme Classification Using Generative Models—0
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss—0
Ranking-Based Autoencoder for Extreme Multi-label Classification—0
Retrieval-augmented Encoders for Extreme Multi-label Text Classification—0
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