Research map / Learning theory and algorithms / Optimal transport
Stochastic Wasserstein Barycenters
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use an adaptive-support, unregularized stochastic barycenter method instead of fixed-support or regularized competitors | Wasserstein barycenter computation · Sharp-feature distributions | support sharpness | Helped |
| Use the proposed method rather than the cited competing method | Wasserstein barycenter computation · Two uniform distributions on the unit square | barycenter support accuracy | Helped |
| Use the proposed sampling-based method for blue-noise generation | Blue-noise sampling · Grayscale image pixels | output quality | No clear effect |
| Use optimized support points instead of iid samples | Super-sample generation · Mixture of ten Gaussians | approximation of distribution shape | Helped |
Closest papers by meaning
- Continuous Regularized Wasserstein Barycenters (NeurIPS 2020 · similarity 0.95)
- Optimal Transport Barycenter via Nonconvex-Concave Minimax Optimization (ICML 2025 · similarity 0.91)
- A Unified Approach for Computing Wasserstein Barycenters of Discrete and Continuous Measures (NeurIPS 2026 · similarity 0.89)
- Estimating Barycenters of Distributions with Neural Optimal Transport (ICML 2024 · similarity 0.89)
- Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows (ICML 2025 · similarity 0.89)
- Energy-Guided Continuous Entropic Barycenter Estimation for General Costs (NeurIPS 2024 · similarity 0.88)
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