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Found 3 papers, 2 papers with code
On the computational complexity of the probabilistic label tree algorithms
First, we show that finding a tree with optimal training cost is NP-complete, nevertheless there are some tractable special cases with either perfect approximation or exact solution that can be obtained in linear time in terms of the number of labels $m$.
A no-regret generalization of hierarchical softmax to extreme multi-label classification
Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels.
Log-time and Log-space Extreme Classification
We present LTLS, a technique for multiclass and multilabel prediction that can perform training and inference in logarithmic time and space.
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