Online probabilistic label trees
arXiv:2007.04451
Abstract
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.
arXiv commentAccepted at AISTATS 2021
References in corpus (9)
- Distilling a Neural Network Into a Soft Decision Tree
- Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
- AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
- Conditional Probability Tree Estimation Analysis and Algorithms
- Logarithmic Time Online Multiclass prediction
- A no-regret generalization of hierarchical softmax to extreme multi-label classification
- Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products
- Learning Optimal Tree Models Under Beam Search
- On the computational complexity of the probabilistic label tree algorithms