A curated list of research works and resources on optimal transport in machine learning. Related Paper.
@article{khamis24OT,
title={Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey},
author={Abdelwahed Khamis and Russell Tsuchida and Mohamed Tarek and Vivien Rolland and Lars Petersson},
year={2024},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher={IEEE}
}
Sections:
- 2021 -
SIAM
Stochastic control liaisons: Richard sinkhorn meets gaspard monge on a schrodinger bridge - 2019 - Computational optimal transport: With applications to data science
- 2015 - Optimal transport for applied mathematicians: calculus of variations, PDEs, and modeling
- 2009 - Optimal transport: old and new
- 2093 - Topics in optimal transportation
- 2006 - On a problem of monge
- 2023 -
CoRL
Watch and match: Supercharging imitation with regularized optimal transport Code - 2022 -
IG
Entropy-regularized 2-wasserstein distance between gaussian measures - 2021 -
ICDM
A regularized wasserstein framework for graph kernels - 2020 -
TMLR
MMD-regularized Unbalanced Optimal Transport Code - 2020 -
ICML
Regularized optimal transport is ground cost adversarial - 2020 -
SIAM
Empirical regularized optimal transport: Statistical theory and applications - 2019 - Quantum entropic regularization of matrix-valued optimal transport
- 2019 -
SIAM
Stabilized sparse scaling algorithms for entropy regularized transport problems - 2018 -
SIAM
Semidual regularized optimal transport - 2018 -
AISTATS
Smooth and sparse optimal transport - 2014 -
NeurIPS
Optimal transport with laplacian regularization - 2014 -
SIAM
Regularized discrete optimal transport Code
- 2022 -
CVPR
A unified framework for implicit sinkhorn differentiation - 2021 -
ICML
Low-rank sinkhorn factorization - 2021 - A note on overrelaxation in the sinkhorn algorithm
- 2020 -
NeurIPS
Faster wasserstein distance estimation with the sinkhorn divergence Code - 2020 -
ICML
On unbalanced optimal transport: An analysis of sinkhorn algorithm Code - 2020 -
NeurIPS
Linear time sinkhorn divergences using positive features - 2019 -
NeurIPS
Massively scalable sinkhorn distances via the nyström method - 2019 -
AISTATS
Interpolating between optimal transport and mmd using sinkhorn divergences - 2019 -
arXiv
Sinkhorn divergences for unbalanced optimal transport - 2019 -
AISTATS
Sample complexity of sinkhorn divergences - 2019 -
ICML
Wasserstein adversarial examples via projected sinkhorn iterations - 2019 -
NeurIPS
Differentiable Ranking and Sorting using Optimal Transport - 2018 -
ICML
Computational optimal transport: Complexity by accelerated gradient descent is better than by sinkhorn’s algorithm - 2018 -
ICLR
Learning latent permutations with gumbel-sinkhorn networks Code - 2017 -
NeurIPS
Overrelaxed sinkhorn-knopp algorithm for regularized optimal transport - 2013 -
NeurIPS
Sinkhorn Distances: Lightspeed Computation of Optimal Transport Code - 2011 - Ranking via sinkhorn propagation
- 2008 -
SIAM
The sinkhorn–knopp algorithm: convergence and applications
- 2022 -
arXiv
Unbalanced optimal transport, from theory to numerics - 2021 -
ICPR
Unbalanced optimal transport in multi-camera tracking applications - 2021 -
AAAI
Learning to count via unbalanced optimal transport - 2020 -
NeurIPS
Partial optimal tranport with applications on positive-unlabeled learning - 2020 -
NeurIPS
Entropic optimal transport between unbalanced gaussian measures has a closed form - 2018 - Unbalanced optimal transport: Dynamic and kantorovich formulations
- 2024 -
ICML
Submodular Framework for Structured-Sparse Optimal Transport Code - 2023 -
ICLR
Sparsity-Constrained Optimal Transport - 2019 -
NeurIPS
Hierarchical optimal transport for document representation Code - 2018 -
AISTATS
Structured optimal transport
- 2021 -
SIAM
Multimarginal optimal transport with a tree-structured cost and the schrodinger bridge problem - 2020 -
ICML
Debiased sinkhorn barycenters - 2020 -
NeurIPS
Continuous regularized wasserstein barycenters Code - 2020 - Multi-marginal optimal transport using partial information with applications in robust localization and sensor fusion
- 2016 -
ACM ToG
Wasserstein barycentric coordinates: Histogram regression using optimal transport - 2015 - Sliced and radon wasserstein barycenters of measures Code
- 2011 -
SIAM
Barycenters in the wasserstein space
- 2023 -
TMLR
Approximating 1-wasserstein distance with trees - 2022 -
AISTATS
Fixed support tree-sliced wasserstein barycenter Code - 2021 -
ICML
Supervised tree-wasserstein distance - 2020 -
NeurIPS
Fast unbalanced optimal transport on a tree Code - 2019 -
NeurIPS
Tree-sliced variants of wasserstein distances Code
- 2022 - A brief survey on computational gromov-wasserstein distance
- 2022 -
arXiv
Gromov-wasserstein autoencoders Code - 2022 -
ICML
Entropic gromov-wasserstein between gaussian distributions - 2021 -
AISTATS
Aligning time series on incomparable spaces - 2021 -
ECML PKDD
Quantized gromov-wasserstein Code - 2020 -
ICML
Gromov-Wasserstein Optimal Transport to Align Single-Cell Multi-Omics Data - 2020 - A contribution to optimal transport on incomparable spaces
- 2019 -
NeurIPS
Asymptotic guarantees for learning generative models with the sliced-wasserstein distance - 2019 -
CVPR
Max-sliced wasserstein distance and its use for gans - 2018 -
EMNLP
Gromov-wasserstein alignment of word embedding spaces Code - 2016 -
ICML
Gromov-wasserstein averaging of kernel and distance matrices Code - 2011 - Gromov–wasserstein distances and the metric approach to object matching
- 2021 -
NeurIPS
Pooling by sliced-wasserstein embedding Code - 2021 -
CVPR
A sliced wasserstein loss for neural texture synthesis Code - 2021 -
ICML
Differentially private sliced wasserstein distance - 2020 -
NeurIPS
Statistical and topological properties of sliced probability divergences - 2019 -
NeurIPS
Sliced gromov-wasserstein Code - 2019 -
NeurIPS
Generalized sliced wasserstein distances - 2016 -
CVPR
Sliced wasserstein kernels for probability distributions
- 2023 -
ECML PKDD
Feature-robust optimal transport for high-dimensional data - 2022 -
ICML
Order constraints in optimal transport Code - 2021 -
ICML
Outlier-robust optimal transport - 2020 -
AISTATS
Unsupervised hierarchy matching with optimal transport over hyperbolic spaces - 2020 -
ICML
A swiss army knife for minimax optimal transport Code - 2020 -
NeurIPS
Co-optimal transport Code - 2017 -
CVPR
Order-preserving wasserstein distance for sequence matching
- 2022 - Estimation of wasserstein distances in the spiked transport model
- 2022 -
NeurIPS
Asymptotics of smoothed wasserstein distances in the small noise regime - 2021 -
NeurIPS
Rates of estimation of optimal transport maps using plug-in estimators via barycentric projections - 2021 -
arXiv
Plugin estimation of smooth optimal transport maps - 2021 -
arXiv
A short proof on the rate of convergence of the empirical measure for the wasserstein distance - 2021 -
NeurIPS
Averaging on the bures-wasserstein manifold: dimension-free convergence of gradient descent - 2021 -
NeurIPS
Dimensionality reduction for wasserstein barycenter - 2020 - Convergence and concentration of empirical measures under wasserstein distance in unbounded functional spaces
- 2020 -
arXiv
A study of performance of optimal transport Code - 2020 -
arXiv
The statistical effect of entropic regularization in optimal transportation - 2019 - Sharp asymptotic and finite-sample rates of convergence of empirical measures in wasserstein distance
- 2019 -
arXiv
Strong equivalence between metrics of wasserstein type - 2018 - Optimal entropy-transport problems and a new hellinger–kantorovich distance between positive measures
- 2016 -
SIAM
A smoothed dual approach for variational wasserstein problems Code
- 2022 -
AMTA
Quantized wasserstein procrustes alignment of word embedding spaces - 2022 -
WIRE CS
Projection-based techniques for high-dimensional optimal transport problems - 2021 -
UAI
Improving approximate optimal transport distances using quantization - 2021 -
MDPI-A
Subspace detours meet gromov–wasserstein - 2021 -
ICML
Projection robust wasserstein barycenters - 2020 -
AISTATS
Gaussian-smoothed optimal transport: Metric structure and statistical efficiency - 2019 -
ICML
Subspace robust wasserstein distances Code - 2019 -
NeurIPS
Subspace detours: Building transport plans that are optimal on subspace projections - 2019 -
NeurIPS
Large-scale optimal transport map estimation using projection pursuit Code
- 2021 -
ICML
Scalable optimal transport in high dimensions for graph distances, embedding alignment, and more Code - 2019 -
SIAM
Stabilized sparse scaling algorithms for entropy regularized transport problems
- 2022 -
NeurIPS
Low-rank optimal transport: Approximation, statistics and debiasing - 2022 -
ICML
Linear-time gromov wasserstein distances using low rank couplings and costs - 2021 -
arXiv
Approximating optimal transport via low-rank and sparse factorization - 2021 - Making transport more robust and interpretable by moving data through a small number of anchor points
- 2019 -
AISTATS
Statistical optimal transport via factored couplings
- 2022 -
arXiv
Budget-constrained bounds for mini-batch estimation of optimal transport - 2022 -
CVPR
Computing wasserstein-p distance between images with linear cost - 2021 - Deep learning and optimal transport: learning from one another
- 2020 -
arXiv
Mrec: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data - 2020 -
AISTATS
Learning with minibatch wasserstein: asymptotic and gradient properties Code - 2019 -
JMLR
Optimal transport: Fast probabilistic approximation with exact solvers. - 2019 -
NeurIPS
Scalable gromov-wasserstein learning for graph partitioning and matching Code - 2017 -
JMLR
Multiscale strategies for computing optimal transport Code - 2011 -
CGF
A multiscale approach to optimal transport
- 2023 -
AISTATS
Rethinking initialization of the sinkhorn algorithm - 2022 -
JMIV
Learning to generate wasserstein barycenters Code - 2022 -
NeurIPS
Supervised training of conditional monge maps - 2022 -
NeurIPS
Wasserstein iterative networks for barycenter estimation Code - 2022 - Kantorovich strikes back! wasserstein gans are not optimal transport?
- 2022 -
AAAI
Efficient optimal transport algorithm by accelerated gradient descent - 2022 -
AAAI
Exploiting problem structure in deep declarative networks: Two case studies - 2022 -
arXiv
Meta optimal transport Code - 2021 -
arXiv
Wasserstein gans work because they fail (to approximate the wasserstein distance) - 2021 -
ICML
Scalable computations of wasserstein barycenter via input convex neural networks - 2021 -
NeurIPS
Do neural optimal transport solvers work? a continuous wasserstein-2 benchmark - 2020 -
ECAI
Speeding up word mover’s distance and its variants via properties of distances between embeddings - 2020 -
UAI
A fast proximal point method for computing exact wasserstein distance - 2020 -
ICML
Optimal transport mapping via input convex neural networks - 2018 -
ICLR
Large-scale optimal transport and mapping estimation Code - 2017 - Linear-complexity relaxed word mover's distance with gpu acceleration
- 2016 -
NeurIPS
Stochastic optimization for large-scale optimal transport Code
- 2022 -
NeurIPS
Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance Code - 2021 -
NeurIPS
Maximum likelihood training of score-based diffusion models Code - 2021 -
NeurIPS
Diffusion schrödinger bridge with applications to score-based generative modeling - 2021 -
ICLR
Distributional sliced-wasserstein and applications to generative modeling - 2021 -
AAAI
Towards generalized implementation of wasserstein distance in gans - 2020 -
NeurIPS
Asymptotic guarantees for generative modeling based on the smooth wasserstein distance - 2020 -
ICLR
Wasserstein-2 generative networks Code - 2019 -
ICML
Learning generative models across incomparable spaces Code - 2018 -
AISTATS
Learning generative models with sinkhorn divergences - 2017 -
ICML
Wasserstein generative adversarial networks Code - 2017 - From optimal transport to generative modeling: the vegan cookbook
- 2017 -
NeurIPS
Improved training of wasserstein gans Code
- 2022 -
ML
Hierarchical optimal transport for unsupervised domain adaptation Code - 2022 -
ICLR
Cross-domain imitation learning via optimal transport Code - 2022 -
IEEE TIP
Few-shot domain adaptation via mixup optimal transport - 2021 -
ICML
Unbalanced minibatch optimal transport; applications to domain adaptation Code - 2021 -
CVPR
Wasserstein contrastive representation distillation - 2021 -
NeurIPS
Lifelong domain adaptation via consolidated internal distribution - 2021 -
CVPR
OTCE: A transferability metric for cross-domain cross-task representations Code - 2021 -
ICCV
The right to talk: An audio-visual transformer approach Code - 2021 -
WACV
Zero-shot recognition via optimal transport - 2020 -
CVPR
Deepemd: Few-shot image classification with differentiable earth mover's distance and structured classifiers Code - 2020 -
ICML
Margin-aware adversarial domain adaptation with optimal transport Code - 2020 -
ECCV
Learning to generate novel domains for domain generalization Code - 2020 -
IJCAI
Joint partial optimal transport for open set domain adaptation. - 2020 -
BMVC
Weakly supervised cross-domain alignment with optimal transport - 2020 -
ICCV
Transporting labels via hierarchical optimal transport for semi-supervised learning - 2020 -
NeurIPS
Geometric dataset distances via optimal transport Code - 2019 -
CVPR
Sliced wasserstein discrepancy for unsupervised domain adaptation Code - 2019 -
AISTATS
Optimal transport for multi-source domain adaptation under target shift Code - 2019 -
NeurIPS
Hierarchical optimal transport for multimodal distribution alignment - 2018 -
AAAI
Wasserstein distance guided representation learning for domain adaptation - 2018 -
ICCV
Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation - 2017 -
ECML PKDD
Theoretical analysis of domain adaptation with optimal transport - 2017 -
NeurIPS
Joint distribution optimal transportation for domain adaptation Code - 2016 -
TPAMI
Optimal transport for domain adaptation Code
-
2022 -
CVPR
Motion-modulated temporal fragment alignment network for few-shot action recognition Action Recognition -
2022 -
AISTATS
Sinkformers: Transformers with doubly stochastic attention Transformers -
2022 -
AISTATS
Proximal optimal transport modeling of population dynamics Code Modeling Dynamics -
2022 -
NeurIPS
Optimal transport of classifiers to fairness Code Fairness in ML -
2022 -
CVPR
Unsupervised action segmentation by joint representation learning and online clustering Action Segmentation -
2021 -
CVPR
Ota: Optimal transport assignment for object detection Code Object Detection -
2021 -
ICCV
Point-set distances for learning representations of 3d point clouds Code Point Cloud -
2021 -
CVPR
A generalized loss function for crowd counting and localization Code Crowd Counting -
2021 -
WACV
Augmented self-labeling for source-free unsupervised domain adaptation Self Labelling -
2021 -
NeurIPS
Measuring generalization with optimal transport Code Generalization in ML -
2021 -
ICLR
Convex potential flows: Universal probability distributions with optimal transport and convex optimization Normalizing Flow -
2020 -
NeurIPS
Model fusion via optimal transport Code Model Fusion -
2020 -
ECCV
Solving the blind perspective-n-point problem end-to-end with robust differentiable geometric optimization Code PnP problem -
2019 -
ICLR
Self-labelling via simultaneous clustering and representation learning Code Self Labelling -
2019 -
ICML
Obtaining fairness using optimal transport theory Code Fairness in ML -
2019 -
ICLR
Learning embeddings into entropic wasserstein spaces Code Embedding -
2015 -
ICML
From word embeddings to document distances Code Document Matching -
2018 -
TPAMI
Visual permutation learning Code Permutation Learning -
Correspondance & Matching:
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Graphs:
- 2022 -
ICML
Learning to predict graphs with fused gromov-wasserstein barycenters - 2020 -
NeurIPS
Copt: Coordinated optimal transport on graphs Code - 2020 -
ICLR
Deep graph matching consensus Code - 2019 -
ICML
Optimal transport for structured data with application on graphs Code - 2019 -
ICML
Gromov-wasserstein learning for graph matching and node embedding Code
- 2022 -
- 2022 -
arXiv
Optimal transport tools (ott): A jax toolbox for all things wasserstein Code - 2021 -
JMLR
Pot: Python optimal transport Code - 2020 -
NeurIPS
Fast geometric learning with symbolic matrices Code