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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
Propensity-scored Probabilistic Label TreesCode1
ECLARE: Extreme Classification with Label Graph CorrelationsCode1
DECAF: Deep Extreme Classification with Label FeaturesCode1
Probabilistic Label Trees for Extreme Multi-label ClassificationCode1
ELIAS: End-to-End Learning to Index and Search in Large Output SpacesCode1
Cluster-Guided Label Generation in Extreme Multi-Label ClassificationCode1
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification FrameworkCode1
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification—0
Content Explorer: Recommending Novel Entities for a Document Writer—0
HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing—0
Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores—0
Label Disentanglement in Partition-based Extreme Multilabel Classification—0
Exploring space efficiency in a tree-based linear model for extreme multi-label classification—0
On-the-fly Global Embeddings Using Random Projections for Extreme Multi-label Classification—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Efficient Text Encoders for Labor Market Analysis—0
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification—0
Adopting the Multi-answer Questioning Task with an Auxiliary Metric for Extreme Multi-label Text Classification Utilizing the Label Hierarchy—0
Fine-grained Generalization Analysis of Vector-valued Learning—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
Block-wise Partitioning for Extreme Multi-label Classification—0
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