Research map / Learning theory and algorithms / Matrix and tensor methods
Sparse Extreme Multi-label Learning with Oracle Property
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace l1 penalty with nonconvex SCAD/MCP penalty in SLEEC | SLEEC embedding regressors · Bibtex, Delicious, Mediamill (medium-sized) | P@1 | Mixed |
| nonconvex penalty (SCAD/MCP) vs l1 in SLEEC | SLEEC · Wiki10, Delicious-L, Amazon (large-scale) | Precision@k and nDCG@k (figures) | Helped |
Closest papers by meaning
- PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification (ICML 2016 · similarity 0.89)
- A no-regret generalization of hierarchical softmax to extreme multi-label classification (NeurIPS 2018 · similarity 0.87)
- Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification (AAAI 2024 · similarity 0.86)
- Tight and Fast Bounds for Multi-Label Learning (ICML 2025 · similarity 0.86)
- Navigating Extremes: Dynamic Sparsity in Large Output Spaces (NeurIPS 2024 · similarity 0.85)
- Generalization Analysis for Multi-Label Learning (ICML 2024 · similarity 0.84)
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