Research map / Learning theory and algorithms
Optimal transport: research map
384 accepted papers on Optimal transport in Learning theory and algorithms, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 7 approaches. The busiest year so far is 2026.
Within Learning theory and algorithms, its share grew from 3.8% in 2023–24 to 5.8% in 2025–26 (84 → 142 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
optimal transport · transport ot · sinkhorn · 256 papers
Approaches in this cluster:
- Neural optimal transport (99 papers)
Learn transport maps and distributions with neural, generative and loss-based formulations. - Transport map estimation (77 papers)
Estimate and regularize transport maps with statistical and low-rank guarantees. - Sinkhorn and entropic OT algorithms (69 papers)
Analyze fast solvers for entropic and unbalanced optimal transport. - Schrodinger bridge matching (11 papers)
Solve entropic OT through Schrodinger bridges with iterative fitting and benchmarks.
Most cited and most cited since 2024:
- Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration (NeurIPS 2017 · 292 citations)
- Neural Architecture Search with Bayesian Optimisation and Optimal Transport (NeurIPS 2018 · 262 citations)
- Hierarchical Multi-Marginal Optimal Transport for Network Alignment (AAAI 2024 · 15 citations)
- An Optimal Transport View for Subspace Clustering and Spectral Clustering (AAAI 2024 · 6 citations)
wasserstein distance · sliced · sw · 128 papers
Approaches in this cluster:
- Wasserstein barycenters and GW (55 papers)
Compute Wasserstein barycenters, projection-robust and Gromov-Wasserstein distances with new optimization methods. - Sliced Wasserstein variants (50 papers)
Develop sliced and tree-sliced Wasserstein distances with faster approximations and kernels. - Wasserstein losses for generative models (23 papers)
Train generative models with Wasserstein and sliced losses, with asymptotic and convergence guarantees.
Most cited and most cited since 2024:
- Gromov-Wasserstein Averaging of Kernel and Distance Matrices (ICML 2016 · 216 citations)
- Sliced Wasserstein Kernel for Persistence Diagrams (ICML 2017 · 151 citations)
- A New Robust Partial p-Wasserstein-Based Metric for Comparing Distributions (ICML 2024 · 3 citations)
- Semidefinite Relaxations of the Gromov-Wasserstein Distance (NeurIPS 2024 · 3 citations)
Related topics in Learning theory and algorithms
- Matrix and tensor methods (1,074)
- Sample complexity (1,363)
- Combinatorial and search optimization (1,492)
- Fairness (516)
- Submodular and game algorithms (1,030)
- Prediction and decision losses (2,134)
- Kernels and regression theory (1,004)
- Clustering (725)
