Research map / Detection and segmentation / Open-set and few-shot detection
Learning to Rank Based on Analogical Reasoning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use analogical preference transfer with BTL aggregation instead of ERR | object ranking · D3 → D4 | ranking loss: 0.147 -> 0.139 | Helped |
| Use analogical preference transfer with BTL aggregation instead of ERR | object ranking · B1 → B2 | ranking loss: 0.144 -> 0.046 | Helped |
| Use analogical preference transfer with BTL aggregation instead of SVM-Rank | object ranking · B1 → B2 | ranking loss: 0.059 -> 0.046 | Helped |
| Use analogical preference transfer with BTL aggregation instead of SVM-Rank | object ranking · B3 → B2 | ranking loss: 0.013 -> 0.000 | Helped |
| Use analogical preference transfer with BTL aggregation instead of ERR | object ranking · F1 → F2 | ranking loss: 0.259 -> 0.225 | Helped |
| Use analogical preference transfer with BTL aggregation instead of SVM-Rank | object ranking · F3 → F4 | ranking loss: 0.169 -> 0.158 | Helped |
| Use analogical preference transfer with BTL aggregation instead of ERR | object ranking · H1 → H2 | ranking loss: 0.084 -> 0.060 | Helped |
| Use analogical preference transfer with BTL aggregation instead of ERR | object ranking · U1 → U2 | ranking loss: 0.163 -> 0.073 | Helped |
| Use analogical preference transfer with BTL aggregation instead of ERR | object ranking · N1 → N2 | ranking loss: 0.090 -> 0.013 | Helped |
| Use analogical preference transfer with BTL aggregation instead of SVM-Rank | object ranking · D1 → D2 | ranking loss: 0.025 -> 0.066 | Hurt |
| Use analogical preference transfer with BTL aggregation instead of SVM-Rank | object ranking · B4 → B2 | ranking loss: 0.026 -> 0.078 | Hurt |
| Use analogical preference transfer with BTL aggregation instead of SVM-Rank | object ranking · V1 → V2 | ranking loss: 0.015 -> 0.030 | Hurt |
Closest papers by meaning
- Active preference learning for ordering items in- and out-of-sample (NeurIPS 2024 · similarity 0.85)
- Just Sort It! A Simple and Effective Approach to Active Preference Learning (ICML 2017 · similarity 0.85)
- Conditional independence and graphical models for rankings (NeurIPS 2026 · similarity 0.85)
- RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank (ICLR 2025 · similarity 0.85)
- Learning List-Level Domain-Invariant Representations for Ranking (NeurIPS 2023 · similarity 0.84)
- Active Ranking without Strong Stochastic Transitivity (NeurIPS 2022 · similarity 0.84)
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