Research map / Learning theory and algorithms / Sample complexity
Accelerated Spectral Ranking
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace self-loop-heavy random walk (RC/LSR) with ASR walk normalized by d_i rather than d_max | spectral ranking random walk · Synthetic MNL/BTL data, random and star graphs | convergence iterations (L1 error): orders of magnitude faster on star graph | Helped |
| Replace RC/LSR walk with ASR walk on real-world data | spectral ranking random walk · YouTube, GIF-anger, SFwork, SFshop (log-likelihood vs iterations) | convergence speed to peak log-likelihood | Helped |
Closest papers by meaning
- Data-driven Rank Breaking for Efficient Rank Aggregation (ICML 2016 · similarity 0.89)
- Optimal Sample Complexity of M-wise Data for Top-K Ranking (NeurIPS 2017 · similarity 0.88)
- On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons (NeurIPS 2019 · similarity 0.88)
- Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons (NeurIPS 2021 · similarity 0.87)
- How Many Pairwise Preferences Do We Need to Rank a Graph Consistently? (AAAI 2019 · similarity 0.87)
- From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking (AAAI 2025 · similarity 0.86)
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