Research map / Learning theory and algorithms / Matrix and tensor methods
Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Gauss-Newton updates instead of competing algorithms | IMC recovery · Synthetic IMC simulations | runtime: 2 to 17 times faster | Helped |
| Use non-local Gauss-Newton updates | GNIMC · IMC recovery | error decay rate: linear -> quadratic | Helped |
| Add side information matrices A and B to the matrix completion problem | IMC · IMC recovery guarantees | sample complexity: \mathcal O(n) -> \mathcal O(\log(n)) | Helped |
| Use a spectral-gap estimator with D=0 instead of nonzero D (ablation) | Rank estimator · Rank estimation simulation | estimated rank: 5 for both values of D | No clear effect |
| Use nonzero D in the rank-gap denominator (ablation) | Rank estimator · Rank estimation simulation | spectral-gap estimation accuracy | Mixed |
| Add imbalance regularization to gradient descent (ablation) | Gradient descent for IMC · IMC simulations | performance | No clear effect |
Closest papers by meaning
- Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow (ICML 2018 · similarity 0.92)
- Fine-grained Generalization Analysis of Inductive Matrix Completion (NeurIPS 2021 · similarity 0.92)
- RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees (NeurIPS 2025 · similarity 0.90)
- Symmetric Matrix Completion with ReLU Sampling (ICML 2024 · similarity 0.90)
- Generalization Analysis of Deep Non-linear Matrix Completion (ICML 2024 · similarity 0.88)
- Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time (ICLR 2024 · similarity 0.88)
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