# Propensity-scored Probabilistic Label Trees

arXiv: https://arxiv.org/abs/2110.10803 · HTML: https://mlsanity.com/paper/2110.10803/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1811.01727/) (2018)
- [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)
- [A no-regret generalization of hierarchical softmax to extreme multi-label classification](https://mlsanity.com/paper/1810.11671/) (2018)
- [PECOS: Prediction for Enormous and Correlated Output Spaces](https://mlsanity.com/paper/2010.05878/) (2020)
- [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)
- [Probabilistic Label Trees for Extreme Multi-label Classification](https://mlsanity.com/paper/2009.11218/) (2020)
- [Conditional Probability Tree Estimation Analysis and Algorithms](https://mlsanity.com/paper/1408.2031/) (2014)
- [Online probabilistic label trees](https://mlsanity.com/paper/2007.04451/) (2020)

## Built on by (most cited first, 8 of 8)

- [On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification](https://mlsanity.com/paper/2207.13186/) (2022)
- [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)
- [Generalized test utilities for long-tail performance in extreme multi-label classification](https://mlsanity.com/paper/2311.05081/) (2023)
- [MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2308.13139/) (2023)
- [Large Language Model as a Teacher for Zero-shot Tagging at Extreme Scales](https://mlsanity.com/paper/2406.09288/) (2024)
- [UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification](https://mlsanity.com/paper/2405.03714/) (2024)
- [On the Necessity of World Knowledge for Mitigating Missing Labels in Extreme Classification](https://mlsanity.com/paper/2408.09585/) (2024)

## Abstract (arXiv)

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. Recently, XMLC has been widely applied to diverse web applications such as automatic content labeling, online advertising, or recommendation systems. In such environments, label distribution is often highly imbalanced, consisting mostly of very rare tail labels, and relevant labels can be missing. As a remedy to these problems, the propensity model has been introduced and applied within several XMLC algorithms. In this work, we focus on the problem of optimal predictions under this model for probabilistic label trees, a popular approach for XMLC problems. We introduce an inference procedure, based on the A^*-search algorithm, that efficiently finds the optimal solution, assuming that all probabilities and propensities are known. We demonstrate the attractiveness of this approach in a wide empirical study on popular XMLC benchmark datasets.