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Optimal transport is a mathematical theory at the interface between optimization and probability theory. It is a natural tool to study probability distributions in the many situations where they appear: data science, partial differential equations, statistics or shape processing.
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![]() Optimal Transportation and Alignment Between Gaussian Measures Sanjit Dandapanthula Aleksandr Podkopaev Shiva Prasad Kasiviswanathan Aaditya Ramdas Ziv Goldfeld | |||
![]() Embedding networks with the random walk first return time distribution Vedanta Thapar Renaud Lambiotte George T. Cantwell | |||
![]() Optimizing Distributional Geometry Alignment with Optimal Transport for Generative Dataset Distillation Xiao Cui Yulei Qin Wengang Zhou Hongsheng Li Houqiang Li | |||
![]() Efficient Transferable Optimal Transport via Min-Sliced Transport Plans Xinran Liu Elaheh Akbari Rocio Diaz Martin Navid NaderiAlizadeh Soheil Kolouri | |||
![]() Hyperbolic Optimal TransportMathematics, Computation and Geometry of Data (MCGD), 2025 | |||
![]() Optimal Transport Based Hyperspectral Unmixing for Highly Mixed ObservationsWorkshop on Hyperspectral Image and Signal Processing (WHISPERS), 2024 | |||
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