Research map / Learning theory and algorithms / Optimal transport
A Swiss Army Knife for Minimax Optimal Transport
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adding stability-based selection of the cost matrix (choosing the most stable cost) | OT cost matrix selection · Color transfer (ocean/sky images) | stability of cost matrices | Helped |
| Cutting-set method with constraint dropping (Algorithm 1) versus solving the full LP | Robust OT with |C|=10 to 90 cost matrices · m=n=100 synthetic | execution time | Helped |
| Stability of cost matrix (noise-sensitivity correlation) | Cost matrices on toy Gaussian and MNIST-UMAP data · Noise sensitivity experiment | noise sensitivity | Helped |
| Using regularized (entropic) transport in Step 2 instead of exact OT (ablation) | Cutting-set algorithm · Synthetic, |C|=40, lambda in {1,0.1,0.01} | convergence | No clear effect |
| Initializing P0 at a subset of vertices of the transportation polytope in the regularized case (ablation) | Cutting-set algorithm with entropic regularization · Synthetic regularized setting | convergence | Hurt |
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