# ECLARE: Extreme Classification with Label Graph Correlations

arXiv: https://arxiv.org/abs/2108.00261 · HTML: https://mlsanity.com/paper/2108.00261/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 52 references that are in the map.
- First posted on arXiv 31 Jul 2021.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest](https://mlsanity.com/paper/2007.03634/) (2020)
- [DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification](https://mlsanity.com/paper/1609.02521/) (2016)
- [Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification](https://mlsanity.com/paper/1904.08249/) (2019)
- [Large-scale Multi-label Learning with Missing Labels](https://mlsanity.com/paper/1307.5101/) (2013)
- [A no-regret generalization of hierarchical softmax to extreme multi-label classification](https://mlsanity.com/paper/1810.11671/) (2018)
- [Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products](https://mlsanity.com/paper/1910.13830/) (2019)
- [Accelerating Extreme Classification via Adaptive Feature Agglomeration](https://mlsanity.com/paper/1905.11769/) (2019)
- [Attentive Neural Architecture Incorporating Song Features For Music Recommendation](https://mlsanity.com/paper/1811.08203/) (2018)

## Built on by (most cited first, 30 of 38)

- [Data Distillation: A Survey](https://mlsanity.com/paper/2301.04272/) (2023)
- [Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2110.00685/) (2021)
- [DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents](https://mlsanity.com/paper/2111.06685/) (2021)
- [NGAME: Negative Mining-aware Mini-batching for Extreme Classification](https://mlsanity.com/paper/2207.04452/) (2022)
- [Infinite Recommendation Networks: A Data-Centric Approach](https://mlsanity.com/paper/2206.02626/) (2022)
- [The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study](https://mlsanity.com/paper/2302.03341/) (2023)
- [Extreme Zero-Shot Learning for Extreme Text Classification](https://mlsanity.com/paper/2112.08652/) (2021)
- [Off-Policy Evaluation for Large Action Spaces via Policy Convolution](https://mlsanity.com/paper/2310.15433/) (2023)
- [CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification](https://mlsanity.com/paper/2211.00640/) (2022)
- [Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification](https://mlsanity.com/paper/2202.05932/) (2022)
- [InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification](https://mlsanity.com/paper/2109.07319/) (2021)
- [On Sampling Collaborative Filtering Datasets](https://mlsanity.com/paper/2201.04768/) (2022)
- [A Survey on Extreme Multi-label Learning](https://mlsanity.com/paper/2210.03968/) (2022)
- [PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation](https://mlsanity.com/paper/2305.12349/) (2023)
- [Dual-Encoders for Extreme Multi-Label Classification](https://mlsanity.com/paper/2310.10636/) (2023)
- [Unified Generative & Dense Retrieval for Query Rewriting in Sponsored Search](https://mlsanity.com/paper/2209.05861/) (2022)
- [Generalized test utilities for long-tail performance in extreme multi-label classification](https://mlsanity.com/paper/2311.05081/) (2023)
- [ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification](https://mlsanity.com/paper/2311.09649/) (2023)
- [MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2308.13139/) (2023)
- [Multi-modal Extreme Classification](https://mlsanity.com/paper/2309.04961/) (2023)
- [Farzi Data: Autoregressive Data Distillation](https://mlsanity.com/paper/2310.09983/) (2023)
- [Learning label-label correlations in Extreme Multi-label Classification via Label Features](https://mlsanity.com/paper/2405.04545/) (2024)
- [Navigating Extremes: Dynamic Sparsity in Large Output Spaces](https://mlsanity.com/paper/2411.03171/) (2024)
- [Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework](https://mlsanity.com/paper/2511.13189/) (2025)
- [Light-weight Deep Extreme Multilabel Classification](https://mlsanity.com/paper/2304.11045/) (2023)
- [StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning](https://mlsanity.com/paper/2308.03000/) (2023)
- [Extreme Multilabel Classification for Specialist Doctor Recommendation with Implicit Feedback and Limited Patient Metadata](https://mlsanity.com/paper/2308.11022/) (2023)
- [Large Language Model as a Teacher for Zero-shot Tagging at Extreme Scales](https://mlsanity.com/paper/2406.09288/) (2024)
- [Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss](https://mlsanity.com/paper/2410.20401/) (2024)
- [Labels in Extremes: How Well Calibrated are Extreme Multi-label Classifiers?](https://mlsanity.com/paper/2411.04276/) (2024)

## Abstract (arXiv)

Deep extreme classification (XC) seeks to train deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. The core utility of XC comes from predicting labels that are rarely seen during training. Such rare labels hold the key to personalized recommendations that can delight and surprise a user. However, the large number of rare labels and small amount of training data per rare label offer significant statistical and computational challenges. State-of-the-art deep XC methods attempt to remedy this by incorporating textual descriptions of labels but do not adequately address the problem. 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. Core contributions of ECLARE include a frugal architecture and scalable techniques to train deep models along with label correlation graphs at the scale of millions of labels. In particular, ECLARE offers predictions that are 2 to 14% more accurate on both publicly available benchmark datasets as well as proprietary datasets for a related products recommendation task sourced from the Bing search engine. Code for ECLARE is available at https://github.com/Extreme-classification/ECLARE.