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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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TitleStatusHype
Semantic Operators: A Declarative Model for Rich, AI-based Data ProcessingCode5
In-Context Learning for Extreme Multi-Label ClassificationCode3
Cluster-Guided Label Generation in Extreme Multi-Label ClassificationCode1
ELIAS: End-to-End Learning to Index and Search in Large Output SpacesCode1
Propensity-scored Probabilistic Label TreesCode1
DECAF: Deep Extreme Classification with Label FeaturesCode1
ECLARE: Extreme Classification with Label Graph CorrelationsCode1
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification FrameworkCode1
Probabilistic Label Trees for Extreme Multi-label ClassificationCode1
Efficient Text Encoders for Labor Market Analysis—0
Retrieval-augmented Encoders for Extreme Multi-label Text Classification—0
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss—0
Exploring space efficiency in a tree-based linear model for extreme multi-label classification—0
GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation—0
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning—0
Multi-label Learning with Random Circular VectorsCode0
UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification—0
Learning label-label correlations in Extreme Multi-label Classification via Label Features—0
ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label ClassificationCode0
Generalized test utilities for long-tail performance in extreme multi-label classificationCode0
Dense Retrieval as Indirect Supervision for Large-space Decision MakingCode0
Dual-Encoders for Extreme Multi-Label ClassificationCode0
Extreme Multi-Label Skill Extraction Training using Large Language Models—0
MDACE: MIMIC Documents Annotated with Code EvidenceCode0
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood AggregationCode0
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