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Optimal transport (OT) theory describes general principles to define and select, among many possible choices, the most efficient way to map a probability measure onto another.
Computational Optimal Transport
G. Peyré and M. Cuturi · 1935
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On the transfer of masses (in Russian)
L. Kantorovich · 1942
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Décomposition polaire et réarrangement monotone des champs de vecteurs
Y. Brenier · 1987
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On a Formula for the L 2 L^{2} Wasserstein Metric between Measures on Euclidean and Hilbert Spaces
M. Gelbrich · 1990
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Review of the Development of Multidimensional Scaling Methods
A. Mead · 1992
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Multitask Learning
R. Caruana · 1997
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A Convexity Principle for Interacting Gases
R. J. McCann · 1997
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Topics in Optimal Transportation , volume 58
C. Villani · 2003
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Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
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Multidimensional Scaling Using Majorization: SMACOF in R
J. De Leeuw and P. Mair · 2009
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Utilizing targeted cancer therapeutic agents in combination: novel approaches and urgent requirements
S. Kummar, H. X. Chen, J. Wright, S. Holbeck, M. D. Millin, J. Tomaszewski, J. Zweibel, J. Collins, and J. H. Doroshow · 2010
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A pilot study of the Histone-Deacetylase inhibitor Givinostat in patients with JAK2V617F positive chronic myeloproliferative neoplasms
A. Rambaldi, C. M. Dellacasa, G. Finazzi, A. Carobbio, M. L. Ferrari, P. Guglielmelli, E. Gattoni, S. Salmoiraghi, M. C. Finazzi, S. Di Tollo, et al · 2010
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Extended-Connectivity Fingerprints
D. Rogers and M. Hahn · 2010
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Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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A Kernel Two-Sample Test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
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Sinkhorn Distances: Lightspeed Computation of Optimal Transport
M. Cuturi · 2013
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Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba · 2014
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Variational Inference with Normalizing Flows
D. Rezende and S. Mohamed · 2015
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Optimal Transport for Applied Mathematicians
F. Santambrogio · 2015
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Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens
A. Dixit, O. Parnas, B. Li, J. Chen, C. P. Fulco, L. Jerby-Arnon, N. D. Marjanovic, D. Dionne, T. Burks, R. Raychowdhury, et al · 2016
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D. Ha, A. Dai, and Q. V. Le · 2016
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Learning Population-Level Diffusions with Generative Recurrent Networks
T. Hashimoto, D. Gifford, and T. Jaakkola · 2016
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Input Convex Neural Networks
B. Amos, L. Xu, and J. Z. Kolter · 2017
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Wasserstein Generative Adversarial Networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Combination therapy in combating cancer
R. B. Mokhtari, T. S. Homayouni, N. Baluch, E. Morgatskaya, S. Kumar, B. Das, and H. Yeger · 2017
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Deep Sets
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola · 2017
Cited alongside, same era.
Semidual Regularized Optimal Transport
M. Cuturi and G. Peyré · 2018
Cited alongside, same era.
Conditional out-of-distribution generation for unpaired data using transfer VAE
M. Lotfollahi, M. Naghipourfar, F. J. Theis, and F. A. Wolf · 2020
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Optimal transport mapping via input convex neural networks
A. Makkuva, A. Taghvaei, S. Oh, and J. Lee · 2020
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Optimal Transport using GANs for Lineage Tracing
N. Prasad, K. Yang, and C. Uhler · 2020
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Self-Supervised Graph Transformer on Large-Scale Molecular Data
Y. Rong, Y. Bian, T. Xu, W. Xie, Y. Wei, W. Huang, and J. Huang · 2020
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Massively multiplex chemical transcriptomics at single-cell resolution
S. R. Srivatsan, J. L. McFaline-Figueroa, V. Ramani, L. Saunders, J. Cao, J. Packer, H. A. Pliner, D. L. Jackson, R. M. Daza, L. Christiansen, et al · 2020
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L. Jacob, J. She, A. Almahairi, S. Rajeswar, and A. Courville · 2018
Cited alongside, same era.
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
L. McInnes, J. Healy, and J. Melville · 2018
Cited alongside, same era.
Wasserstein dictionary learning: Optimal transport-based unsupervised nonlinear dictionary learning
M. A. Schmitz, M. Heitz, N. Bonneel, F. Ngole, D. Coeurjolly, M. Cuturi, G. Peyré, and J.-L. Starck · 2018
Cited alongside, same era.
Drug and disease signature integration identifies synergistic combinations in glioblastoma
V. Stathias, A. M. Jermakowicz, M. E. Maloof, M. Forlin, W. Walters, R. K. Suter, M. A. Durante, S. L. Williams, J. W. Harbour, C.-H. Volmar, et al · 2018
Cited alongside, same era.
SCANPY: large-scale single-cell gene expression data analysis
F. A. Wolf, P. Angerer, and F. J. Theis · 2018
Cited alongside, same era.
Learning Action Representations for Reinforcement Learning
Y. Chandak, G. Theocharous, J. Kostas, S. Jordan, and P. Thomas · 2019
Cited alongside, same era.
Predicting cell lineages using autoencoders and optimal transport
K. D. Yang, K. Damodaran, S. Venkatachalapathy, A. C. Soylemezoglu, G. Shivashankar, and C. Uhler · 2020
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Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
D. Alvarez-Melis, Y. Schiff, and Y. Mroueh · 2021
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Learning Single-Cell Perturbation Responses using Neural Optimal Transport
C. Bunne, S. G. Stark, G. Gut, J. S. del Castillo, K.-V. Lehmann, L. Pelkmans, A. Krause, and G. Ratsch · 2021
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Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
J. Fan, A. Taghvaei, and Y. Chen · 2021
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Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
C.-W. Huang, R. T. Q. Chen, C. Tsirigotis, and A. Courville · 2021
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Minimax estimation of smooth optimal transport maps
J.-C. Hütter and P. Rigollet · 2021
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Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
A. Korotin, L. Li, A. Genevay, J. M. Solomon, A. Filippov, and E. Burnaev · 2021
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Compositional perturbation autoencoder for single-cell response modeling
M. Lotfollahi, A. K. Susmelj, C. De Donno, Y. Ji, I. L. Ibarra, F. A. Wolf, N. Yakubova, F. J. Theis, and D. Lopez-Paz · 2021
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Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks
R. K. Mahabadi, S. Ruder, M. Dehghani, and J. Henderson · 2021
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Large-Scale Wasserstein Gradient Flows
P. Mokrov, A. Korotin, L. Li, A. Genevay, J. Solomon, and E. Burnaev · 2021
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Entropic estimation of optimal transport maps
A.-A. Pooladian and J. Niles-Weed · 2021
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Input Convex Gradient Networks
J. Richter-Powell, J. Lorraine, and B. Amos · 2021
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Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein
M. Cuturi, L. Meng-Papaxanthos, Y. Tian, C. Bunne, G. Davis, and O. Teboul · 2022
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On the sample complexity of entropic optimal transport
P. Rigollet and A. J. Stromme · 2022
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Machine intelligence for chemical reaction space
P. Schwaller, A. C. Vaucher, R. Laplaza, C. Bunne, A. Krause, C. Corminboeuf, and T. Laino · 2022
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