Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features
Author:
Affiliation:
1. University of California, Los Angeles, Los Angeles, USA
2. Aalto University, Espoo, Finland
3. Aalto University & University of Bath, Espoo, Finland
Funder
Research Council of Finland
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3672063
Reference45 articles.
1. Zipf's law and the Internet;Adamic Lada A;Glottometrics,2002
2. Anonymous. 2024. Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction. In The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=6ARlSgun7J
3. R. Babbar and B. Schölkopf. 2017. DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification. In WSDM.
4. Data scarcity, robustness and extreme multi-label classification
5. Cluster-GCN
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