# On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification

arXiv: https://arxiv.org/abs/2207.13186 · HTML: https://mlsanity.com/paper/2207.13186/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1811.01727/) (2018)
- [Inferring Networks of Substitutable and Complementary Products](https://mlsanity.com/paper/1506.08839/) (2015)
- [Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback](https://mlsanity.com/paper/1909.03601/) (2019)
- [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)
- [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)
- [Learning Optimal Tree Models Under Beam Search](https://mlsanity.com/paper/2006.15408/) (2020)
- [Conditional Probability Tree Estimation Analysis and Algorithms](https://mlsanity.com/paper/1408.2031/) (2014)
- [Propensity-scored Probabilistic Label Trees](https://mlsanity.com/paper/2110.10803/) (2021)
- [Unbiased Loss Functions for Multilabel Classification with Missing Labels](https://mlsanity.com/paper/2109.11282/) (2021)

## Built on by (most cited first, 15 of 15)

- [CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification](https://mlsanity.com/paper/2211.00640/) (2022)
- [A Survey on Extreme Multi-label Learning](https://mlsanity.com/paper/2210.03968/) (2022)
- [Towards Memory-Efficient Training for Extremely Large Output Spaces -- Learning with 500k Labels on a Single Commodity GPU](https://mlsanity.com/paper/2306.03725/) (2023)
- [Generalized test utilities for long-tail performance in extreme multi-label classification](https://mlsanity.com/paper/2311.05081/) (2023)
- [Consistent algorithms for multi-label classification with macro-at-k metrics](https://mlsanity.com/paper/2401.16594/) (2024)
- [MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2308.13139/) (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)
- [Labels in Extremes: How Well Calibrated are Extreme Multi-label Classifiers?](https://mlsanity.com/paper/2411.04276/) (2024)
- [Single-sample versus case-control sampling scheme for Positive Unlabeled data: the story of two scenarios](https://mlsanity.com/paper/2312.02095/) (2023)
- [UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification](https://mlsanity.com/paper/2405.03714/) (2024)
- [Determined Multi-Label Learning via Similarity-Based Prompt](https://mlsanity.com/paper/2403.16482/) (2024)
- [A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends](https://mlsanity.com/paper/2406.06119/) (2024)
- [Augmented prediction of a true class for Positive Unlabeled data under selection bias](https://mlsanity.com/paper/2407.10309/) (2024)
- [On the Necessity of World Knowledge for Mitigating Missing Labels in Extreme Classification](https://mlsanity.com/paper/2408.09585/) (2024)

## Abstract (arXiv)

The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this approach showing that despite its theoretical soundness, its application in contemporary XMLC works is debatable. We exhaustively discuss the flaws of the propensity-based approach, and present several recipes, some of them related to solutions used in search engines and recommender systems, that we believe constitute promising alternatives to be followed in XMLC.