====== Extreme Multi-Label Classification ======
Extreme multi-label classification (or extreme multi-label learning, XML) is the task of matching an input with 0 or more labels (the most relevant labels) from an extremely large label set.  The space of outputs is $2^L$, where $L$ is a large set.  This is different from multi-class classification, where each instance has only one associated label.  It has been used for recommendations and product search.  Search and IR can also be formulated as XML.

===== Overviews =====
  * [[https://arxiv.org/pdf/2210.03968|Wei et al 2022 - A Survey on Extreme Multi-label Learning]]
  * [[https://arxiv.org/pdf/2401.16549|Tarekegn et al 2024 - Deep Learning for Multi-Label Learning: A Comprehensive Survey]]

===== Papers =====
  * [[https://dl.acm.org/doi/pdf/10.1145/3206025.3206030|Zhang et al 2018 - Deep Extreme Multi-label Learning]]
  * [[https://arxiv.org/pdf/1811.01727|You et al 2018 - AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification]]
  * **Applications**
    * [[https://arxiv.org/pdf/2106.12657|Chang et al 2021 - Extreme Multi-label Learning for Semantic Matching in Product Search]]

===== Related Pages =====
  * [[Classification]]