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- W2995092912 abstract "Optimal transport (OT) offers a unifying theory to model and solve many problems that involve probability distributions. Because of the ubiquity of probability distributions in data sciences, this theory has recently attracted the interest of practitioners, motivating the proposal of faster algorithms and calling for more research into its statistical underpinnings. The theory and its algorithms are currently used to handle problems that involve distributions supported on low dimensional spaces (such as shape registration problems in medical imaging or regression problems in neuroscience), but also in high dimensional spaces (such as supervised cost-sensitive classification and training of generative models in machine learning, comparison of word embeddings in language processing and cell populations in single-cell genomics). This positive feedback loop, from theory, optimization to modeling is opening new application perspectives and raises naturally the many open challenges that remain to turn OT in a scalable tool, able to cope with huge datasets, yet also able to offer meaningful results, derived from sharp theoretical analyses of its limitations. This special issue on optimal transport in data science presents a varied set of contributions aiming at covering this very active research field, ranging from theoretical advances in probability and statistics to exciting applications in imaging and learning." @default.
- W2995092912 created "2019-12-26" @default.
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- W2995092912 date "2019-12-01" @default.
- W2995092912 modified "2023-10-18" @default.
- W2995092912 title "Editorial IMA IAI - Information and Inference special issue on optimal transport in data sciences" @default.
- W2995092912 doi "https://doi.org/10.1093/imaiai/iaz032" @default.
- W2995092912 hasPublicationYear "2019" @default.
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