Research map / Learning theory and algorithms / Optimal transport
Schrodinger bridge matching
Solve entropic OT through Schrodinger bridges with iterative fitting and benchmarks.
11 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2023–2026) · an approach in Optimal transport, keyword cluster “optimal transport · transport ot · sinkhorn”
State History Tried Preprints Who Export
Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
5 papers in 2023–24 and 6 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 6.0% to 4.2%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2023 · Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters (NeurIPS 2023 · 5 citations)
- 2024 · Light Schrödinger Bridge (ICLR 2024 · 0 citations)
- 2025 · Schrödinger Bridge Matching for Tree-Structured Costs and Entropic Wasserstein Barycentres (NeurIPS 2025 · 0 citations)
- 2026 · Entering the Era of Discrete Diffusion Models: A Benchmark for Schrödinger Bridges and Entropic Optimal Transport (ICLR 2026 · 0 citations)
Earliest papers here
- Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters (NeurIPS 2023 · 5 citations)
- Entropic Neural Optimal Transport via Diffusion Processes (NeurIPS 2023 · 2 citations)
- Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark (NeurIPS 2023 · 1 citation)
What has been tried
9 papers here report 70 measured changes (a component added, removed or swapped, compared with a baseline): helped 66%, no clear effect 7%, hurt 20%, mixed 7%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| Use independent, minibatch OT, or ground-truth transport plan instead of requiring the EOT plan | EOT/SB benchmark · cB W₂²-UVP (%) | 0.18; 0.1; 0.1 | No clear effect | Light and Optimal Schrödinger Bridge Matching (ICML 2024 · 0 citations) |
Recent preprints
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Who publishes on it
Institutions on the most papers of this approach since 2023 (of 11 papers; from OpenAlex and the papers' affiliations).
- Skolkovo Institute of Science and Technology · 4 papers
- École Polytechnique · 2 papers
- University of Oxford · 2 papers
Use it
Download BibTeX of 11 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- Neural optimal transport (99 papers)
- Transport map estimation (77 papers)
- Sinkhorn and entropic OT algorithms (69 papers)
Elsewhere in the topic
- Wasserstein barycenters and GW (55 papers)
- Sliced Wasserstein variants (50 papers)
- Wasserstein losses for generative models (23 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).