Research map / Learning theory and algorithms / Optimal transport
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use C W2B instead of CR WB | Continuous Wasserstein-2 barycenter estimation · Location-scatter Gaussian, D=256, N=4 | L2-UVP, %: >100 -> 0.83 | Helped |
| Use C W2B instead of SC W2B | Continuous Wasserstein-2 barycenter estimation · Location-scatter Gaussian, D=256, N=4 | L2-UVP, %: 1.50 -> 0.83 | Helped |
| Use C W2B instead of CR WB | Continuous Wasserstein-2 barycenter estimation · Location-scatter uniform, D=256, N=4 | L2-UVP, %: $>100$ -> $0.82$ | Helped |
| Use C W2B instead of SC W2B | Continuous Wasserstein-2 barycenter estimation · Location-scatter uniform, D=256, N=4 | L2-UVP, %: $1.52$ -> $0.82$ | Helped |
| Use C W2B instead of SC W2B | Continuous Wasserstein-2 barycenter estimation · Location-scatter uniform, D=32, N=20 | L2-UVP, %: $0.60$ -> $0.31$ | Helped |
| Use C W2B instead of CR WB | Subset posterior aggregation · Poisson regression | B W2^2-UVP, %: $1.53$ -> $0.1$ | Helped |
| Use C W2B instead of SC W2B generated barycenter | Subset posterior aggregation · Poisson regression | B W2^2-UVP, %: $0.67$ -> $0.1$ | Helped |
| Use C W2B instead of CR WB | Subset posterior aggregation · Negative binomial regression | B W2^2-UVP, %: $1.26$ -> $0.11$ | Helped |
| Use C W2B instead of SC W2B generated barycenter | Subset posterior aggregation · Negative binomial regression | B W2^2-UVP, %: $0.15$ -> $0.11$ | Helped |
Closest papers by meaning
- A Unified Approach for Computing Wasserstein Barycenters of Discrete and Continuous Measures (NeurIPS 2026 · similarity 0.92)
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