Extreme Multi-Label Classification

Description from the archive archive 2025-07-28.

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2 datasets whose archive record lists this task, ordered by the archive's paper count.

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Most implemented papers archive 2025-07-28

30 shown of 31 papers with code (75 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
  29 Oct 2021 4 repositories listed Syntology ran 6 of 20 samples · 14 unverified
  We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework.
• Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
  17 Apr 2019 3 repositories listed
  In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.
• Semantic Operators: A Declarative Model for Rich, AI-based Data Processing
  16 Jul 2024 2 repositories listed
  Our model specifies the expected behavior of each operator with a high-quality gold algorithm, and we develop an optimization framework that reduces cost, while providing accuracy guarantees with respect to a gold…
• In-Context Learning for Extreme Multi-Label Classification
  22 Jan 2024 2 repositories listed
  Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precise classes or how to assign them, and it…
• Generalized test utilities for long-tail performance in extreme multi-label classification
  9 Nov 2023 2 repositories listed Syntology ran 17 of 40 samples · 23 unverified
  As such, it is characterized by long-tail labels, i.
• Stratified Sampling for Extreme Multi-Label Data
  5 Mar 2021 2 repositories listed
  Extreme multi-label classification (XML) is becoming increasingly relevant in the era of big data.
• Probabilistic Label Trees for Extreme Multi-label Classification
  23 Sep 2020 2 repositories listed
  We first introduce the PLT model and discuss training and inference procedures and their computational costs.
• Taming Pretrained Transformers for Extreme Multi-label Text Classification
  7 May 2019 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue.
• DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
  8 Sep 2016 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size.
• Multi-label Learning with Random Circular Vectors
  8 Jul 2024 1 repository listed
  The extreme multi-label classification~(XMC) task involves learning a classifier that can predict from a large label set the most relevant subset of labels for a data instance.
• ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification
  16 Nov 2023 1 repository listed
  This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to predict multiple labels for each instance from an extremely large label space.
• Dense Retrieval as Indirect Supervision for Large-space Decision Making
  28 Oct 2023 1 repository listed
  Many discriminative natural language understanding (NLU) tasks have large label spaces.
• Dual-Encoders for Extreme Multi-Label Classification
  16 Oct 2023 1 repository listed
  We propose decoupled softmax loss - a simple modification to the InfoNCE loss - that overcomes the limitations of existing contrastive losses.
• MDACE: MIMIC Documents Annotated with Code Evidence
  7 Jul 2023 1 repository listed
  In this paper, we introduce MDACE, the first publicly available code evidence dataset, which is built on a subset of the MIMIC-III clinical records.
• PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
  21 May 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training…
• Cluster-Guided Label Generation in Extreme Multi-Label Classification
  17 Feb 2023 1 repository listed
  For extreme multi-label classification (XMC), existing classification-based models poorly perform for tail labels and often ignore the semantic relations among labels, like treating "Wikipedia" and "Wiki" as independent…
• ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
  16 Oct 2022 1 repository listed Syntology ran 7 of 11 samples · 4 unverified · 11 pointer-only (licence)
  A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this index via beam search.
• Propensity-scored Probabilistic Label Trees
  20 Oct 2021 1 repository listed Syntology ran 0 of 9 samples · 9 unverified
  Extreme multi-label classification (XMLC) refers to the task of tagging instances with small subsets of relevant labels coming from an extremely large set of all possible labels.
• Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers
  13 Oct 2021 1 repository listed
  We address these challenges with Adaptive SGD, an adaptive elastic model averaging stochastic gradient descent algorithm for heterogeneous multi-GPUs that is characterized by dynamic scheduling, adaptive batch size…
• DECAF: Deep Extreme Classification with Label Features
  1 Aug 2021 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer…
• ECLARE: Extreme Classification with Label Graph Correlations
  31 Jul 2021 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds.
• Extreme Multi-label Learning for Semantic Matching in Product Search
  23 Jun 2021 1 repository listed
  In this paper, we aim to improve semantic product search by using tree-based XMC models where inference time complexity is logarithmic in the number of products.
• Priberam at MESINESP Multi-label Classification of Medical Texts Task
  12 May 2021 1 repository listed
  Information retrieval tools are crucial in order to navigate and provide meaningful recommendations for articles and treatments.
• Top-k eXtreme Contextual Bandits with Arm Hierarchy
  15 Feb 2021 1 repository listed
  We show that our algorithm has a regret guarantee of O(k√((A-k+1)T log(|ℱ|T))), where A is the total number of arms and ℱ is the class containing the regression function, while only requiring Õ(A) computation per time…
• Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework
  1 Dec 2020 1 repository listed
  We introduce a deep learning model to learn the set of enumerated job skills associated with a job description.
• A no-regret generalization of hierarchical softmax to extreme multi-label classification
  27 Oct 2018 1 repository listed
  Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels.
• HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization
  1 Oct 2018 1 repository listed
  The lower the HS level, the less the categorization performance.
• Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
  26 Jun 2018 1 repository listed
  Our experiments show that there is indeed additional structure beyond sparsity in the real datasets; our method is able to discover it and exploit it to create excellent reconstructions with fewer measurements (by a…
• Adversarial Extreme Multi-label Classification
  5 Mar 2018 1 repository listed
  The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels.
• Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification
  12 Feb 2018 1 repository listed
  Finally, we show that the Sparse Weighted Nearest-Neighbor Method can process data points in real time on XMLC datasets with equivalent performance to SOTA models, with a single thread and smaller storage footprint.

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.