Research map / Learning theory and algorithms / Matrix and tensor methods
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use weighted rank-one PGD skill estimation instead of majority voting | weighted-majority label aggregation · Web | prediction error: 0.2693 -> 0.1485 | Helped |
| use PGD instead of majority voting | label aggregation · RTE1 | prediction error: 0.1031 -> 0.07 | Helped |
| use PGD instead of Opt-D&S | label aggregation · Dogs | prediction error: 0.1689 -> 0.1660 | Helped |
| use PGD instead of ER | label aggregation · Temp | prediction error: 0.052 -> 0.0512 | Helped |
| use PGD instead of KOS | label aggregation · RTE1 | prediction error: 0.3975 -> 0.07 | Helped |
| use PGD instead of Opt-D&S on sparse ring graph | label aggregation · Synthetic ring graph | prediction error | Helped |
| increase number of tasks | PGD · Synthetic graphs (clique, star, ring) | average prediction error | Helped |
| sparsify worker interaction graph (lower maximum degree) | PGD · RTE1 and Temp | average prediction error | Hurt |
Closest papers by meaning
- Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion (NeurIPS 2020 · similarity 0.89)
- Exact Exponent in Optimal Rates for Crowdsourcing (ICML 2016 · similarity 0.87)
- Symmetric Matrix Completion with ReLU Sampling (ICML 2024 · similarity 0.86)
- Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization (ICML 2021 · similarity 0.86)
- In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting (AAAI 2025 · similarity 0.84)
- Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting (NeurIPS 2024 · similarity 0.84)
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