Research map / Label-efficient and robust learning / Recognition and long-tail
HodgeRank With Information Maximization for Crowdsourced Pairwise Ranking Aggregation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use active sampling instead of passive random sampling | HodgeRank pairwise ranking · Simulated data, n=16 | Kendall's tau | Helped |
| Use supervised Bayesian information maximization instead of random sampling | HodgeRank pairwise ranking · LIVE VQA | Kendall's tau: roughly 5% | Helped |
| Use supervised Bayesian information maximization instead of random sampling | HodgeRank pairwise ranking · IQA dataset | Kendall's tau | Helped |
| Use supervised Bayesian information maximization instead of random sampling | HodgeRank pairwise ranking · Reading level dataset | Kendall's tau | Helped |
| Use online Sherman-Morrison-Woodbury posterior updates instead of offline supervised computation | Supervised Bayesian active sampling · Simulated data | computational complexity: 25.22 -> 0.10 | Helped |
| Use the supervised method instead of Crowd-BT | Pairwise ranking aggregation · VQA dataset | running cost (s): 600 -> 18 | Helped |
| Use the supervised method instead of Crowd-BT | Pairwise ranking aggregation · IQA dataset | running cost (s): 480 -> 12 | Helped |
| Use the supervised method instead of Crowd-BT | Pairwise ranking aggregation · Reading level dataset | running cost (s): 4200 -> 120 | Helped |
| Use a larger regularization parameter for sparse graphs (ablation) | Supervised Bayesian active sampling · Reading level dataset | Kendall's tau | Helped |
| Vary gamma on dense graphs (ablation) | Supervised Bayesian active sampling · Dense graphs | Kendall's tau | No clear effect |
Closest papers by meaning
- Exact Exponent in Optimal Rates for Crowdsourcing (ICML 2016 · similarity 0.87)
- Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers (ICML 2018 · similarity 0.86)
- Optimality of Belief Propagation for Crowdsourced Classification (ICML 2016 · similarity 0.86)
- Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms (NeurIPS 2019 · similarity 0.86)
- Label Aggregation for Composite Crowd Tasks by Worker Ability Constraint Satisfaction (AAAI 2025 · similarity 0.83)
- Uncertainty Herding: One Active Learning Method for All Label Budgets (ICLR 2025 · similarity 0.83)
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