Research map / Learning theory and algorithms / Prediction and decision losses
Secretary Ranking with Minimal Inversions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use noisy rank estimates and assign each element to the nearest empty position | Secretary ranking · Dense case (m=n) | expected inversions: trivial algorithm: \Theta(n^2); Dense Ranking: O(n\sqrt{n}) | Helped |
| Use binary-tree allocation with sufficiently many positions | Secretary ranking · Sparse case, m ≥ n^{2.988 + ε} | probability of zero inversions: 1-o(1) | Helped |
| Combine binary-tree allocation with dense ranking within leaf blocks | Secretary ranking · General case, m ≥ 10 n log n | expected inversions: \tilde{O}(n^{1.5 - 0.5 g(\alpha)}) | Helped |
Closest papers by meaning
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- Ranking with Multiple Oracles: From Weak to Strong Stochastic Transitivity (ICML 2025 · similarity 0.84)
- Sampling for Beyond-Worst-Case Online Ranking (AAAI 2024 · similarity 0.82)
- Fair Secretaries with Unfair Predictions (NeurIPS 2024 · similarity 0.81)
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