Research map / Learning theory and algorithms / Prediction and decision losses
Active Learning for Cost-Sensitive Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace passive online CSOAA learner with COAL active query rule | online linear cost-sensitive regression · ImageNet 40 and RCV1-v2 simulated active learning | test cost vs number of queries: factor of 16 fewer queries | Helped |
| use COAL instead of passive learning inside AGGRAVATE | learning-to-search · Wikipedia sequence labeling | number of roll-outs to reach passive accuracy: factor of 4 fewer roll-outs | Helped |
| use COAL instead of passive learning inside AGGRAVATE | learning-to-search · POS tagging | number of roll-outs | No clear effect |
| replace COAL query rule with all-or-none querying (ablation) | COAL · ImageNet 40 simulated AL | test cost vs queries | Hurt |
| query only by cost-range condition without domination check (NODOM) (ablation) | COAL · ImageNet 40 simulated AL | test cost vs queries | Hurt |
Closest papers by meaning
- Towards Cost-Effective Learning: A Synergy of Semi-Supervised and Active Learning (CVPR 2025 · similarity 0.86)
- Uncertainty Herding: One Active Learning Method for All Label Budgets (ICLR 2025 · similarity 0.85)
- Instance-wise Supervision-level Optimization in Active Learning (CVPR 2025 · similarity 0.85)
- Learning with Selectively Labeled Data from Multiple Decision-makers (ICML 2025 · similarity 0.85)
- Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios (NeurIPS 2024 · similarity 0.85)
- Fast Co-Training under Weak Dependence via Stream-Based Active Learning (ICML 2024 · similarity 0.84)
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