Research map / Learning theory and algorithms / Optimal transport
CO-Optimal Transport
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use COOT instead of SGW | Caltech-Office HDA · Semi-supervised HDA (n_t=3), mean | accuracy: 84.65$$\pm 6.62 -> 89.47$$\pm 4.74 | Helped |
| Use COOT instead of KCCA | Caltech-Office HDA · Unsupervised HDA (n_t=0), mean | accuracy: 18.72}$$\pm 5.33 -> 33.28$$\pm 7.61 | Helped |
| Use COOT instead of CCOT-GW | Co-clustering · D1 | CCE: .004\pm{.002} -> \mathbf{0} | Helped |
| Use COOT instead of CCOT-GW | Co-clustering · D2 | CCE: .011\pm{.056} -> \mathbf{.009\pm{0.04}} | Helped |
| Use COOT instead of CCOT-GW | Co-clustering · D3 | CCE: \mathbf{.008\pm{.001}} -> $.04\pm{.05} | Hurt |
| Use COOT instead of CCOT-GW | Co-clustering · D4 | CCE: .079\pm{.031} -> \mathbf{0.068\pm{0.04}} | Helped |
Closest papers by meaning
- Unbalanced CO-optimal Transport (AAAI 2023 · similarity 0.93)
- Optimal Tensor Transport (AAAI 2022 · similarity 0.91)
- Hierarchical Refinement: Optimal Transport to Infinity and Beyond (ICML 2025 · similarity 0.88)
- A Biconvex Formulation for Stable Transport of Mixture Models with a Unique Solution (NeurIPS 2026 · similarity 0.87)
- Geometry-Aware Optimal Transport: Fast Intrinsic Dimension and Wasserstein Distance Estimation (NeurIPS 2026 · similarity 0.86)
- Double-Bounded Optimal Transport for Advanced Clustering and Classification (AAAI 2024 · similarity 0.86)
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