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End-to-End Learning to Index and Search in Large Output Spaces
Nilesh Gupta · Patrick Chen · Hsiang-Fu Yu · Cho-Jui Hsieh · Inderjit Dhillon

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #143
Extreme multi-label classification (XMC) is a popular framework for solving many real-world problems that require accurate prediction from a very large number of potential output choices. A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this index via beam search. Existing methods initialize the tree index by clustering the label space into a few mutually exclusive clusters based on pre-defined features and keep it fixed throughout the training procedure. This approach results in a sub-optimal indexing structure over the label space and limits the search performance to the quality of choices made during the initialization of the index. In this paper, we propose a novel method ELIAS which relaxes the tree-based index to a specialized weighted graph-based index which is learned end-to-end with the final task objective. More specifically, ELIAS models the discrete cluster-to-label assignments in the existing tree-based index as soft learnable parameters that are learned jointly with the rest of the ML model. ELIAS achieves state-of-the-art performance on several large-scale extreme classification benchmarks with millions of labels. In particular, ELIAS can be up to 2.5% better at precision@$1$ and up to 4% better at recall@$100$ than existing XMC methods. A PyTorch implementation of ELIAS along with other resources is available at https://github.com/nilesh2797/ELIAS.

Author Information

Nilesh Gupta (University of Texas at Austin)
Nilesh Gupta

PhD student at UT Austin working on large-scale machine learning

Patrick Chen (UCLA)
Hsiang-Fu Yu (Amazon)
Cho-Jui Hsieh (UCLA, Amazon)
Inderjit Dhillon (Google & UT Austin)

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