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pro vyhledávání: '"Carla Tameling"'
Publikováno v:
SIAM Journal on Mathematics of Data Science. 2:419-443
We derive limit distributions for certain empirical regularized optimal transport distances between probability distributions supported on a finite metric space and show consistency of the (naive) bootstrap. In particular, we prove that the empirical
We consider a class of infinite-dimensional optimization problems in which a distributed vector-valued variable should pointwise almost everywhere take values from a given finite set $\mathcal{M}\subset\mathbb{R}^m$. Such hybrid discrete--continuous
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https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85130609473
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Autor:
Carla Tameling, Axel Munk
Publikováno v:
DSW
In this paper we discuss some recent limit laws for empirical optimal transport distances from a simulation perspective. On discrete spaces, this requires to solve another optimal transport problem in each simulation step, which reveals simulations o
Publikováno v:
Ann. Appl. Probab. 29, no. 5 (2019), 2744-2781
We derive distributional limits for empirical transport distances between probability measures supported on countable sets. Our approach is based on sensitivity analysis of optimal values of infinite dimensional mathematical programs and a delta meth
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http://arxiv.org/abs/1707.00973
http://arxiv.org/abs/1707.00973
We consider a class of (ill-posed) optimal control problems in which a distributed vector-valued control is enforced to pointwise take values in a finite set $\mathcal{M}\subset\mathbb{R}^m$. After convex relaxation, one obtains a well-posed optimiza
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