Online probabilistic label trees
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Abstract (arXiv)
We introduce online probabilistic label trees (OPLTs), an algorithm that trains a label tree classifier in a fully online manner without any prior knowledge about the number of training instances, their features and labels. OPLTs are characterized by low time and space complexity as well as strong theoretical guarantees. They can be used for online multi-label and multi-class classification, including the very challenging scenarios of one- or few-shot learning. We demonstrate the attractiveness of OPLTs in a wide empirical study on several instances of the tasks mentioned above.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 3.
5 papers.
- 2021Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classificationcited by 66
- 2021Propensity-scored Probabilistic Label Treescited by 8
- 2023PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregationcited by 7
- 2023Improving Dual-Encoder Training through Dynamic Indexes for Negative Miningcited by 5
- 2023Enhancing Group Fairness in Online Settings Using Oblique Decision Forestscited by 3
- 2023Enhancing Group Fairness in Online Settings Using Oblique Decision Forests17 Oct 2023
- 2023PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation21 May 2023
- 2023Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining27 Mar 2023
- 2021Propensity-scored Probabilistic Label Trees20 Oct 2021
- 2021Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification1 Oct 2021