Research map / Learning theory and algorithms / Prediction and decision losses
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use individualized Split LBI instead of the strongest reported baseline | Partial ranking prediction · Simulated dataset | Micro-F1: .801 -> .834 | Helped |
| Use individualized Split LBI instead of the strongest reported baseline | Partial ranking prediction · Simulated dataset | Macro-F1: .660 -> .761 | Helped |
| Use individualized Split LBI instead of the strongest reported baseline | Partial ranking prediction · Human Age | Micro-F1: .741 -> .779 | Helped |
| Use individualized Split LBI instead of the strongest reported baseline | Partial ranking prediction · Human Age | Macro-F1: .628 -> .694 | Helped |
| Use individualized Split LBI instead of the strongest reported baseline | Partial ranking prediction · WorldCollege Ranking | Micro-F1: .552 -> .649 | Helped |
| Use individualized Split LBI instead of the strongest reported baseline | Partial ranking prediction · WorldCollege Ranking | Macro-F1: .513 -> .645 | Helped |
Closest papers by meaning
- LBD: Decouple Relevance and Observation for Individual-Level Unbiased Learning to Rank (NeurIPS 2022 · similarity 0.86)
- Data-driven Rank Breaking for Efficient Rank Aggregation (ICML 2016 · similarity 0.85)
- Who Likes What? — SplitLBI in Exploring Preferential Diversity of Ratings (AAAI 2020 · similarity 0.85)
- From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking (AAAI 2025 · similarity 0.84)
- Selective Preference Aggregation (ICML 2025 · similarity 0.83)
- Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank (ICML 2024 · similarity 0.83)
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