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pro vyhledávání: '"Sanz, Alberto"'
Autor:
González-Sanz, Alberto, Nutz, Marcel
The quadratically regularized optimal transport problem is empirically known to have sparse solutions: its optimal coupling $\pi_{\varepsilon}$ has sparse support for small regularization parameter $\varepsilon$, in contrast to entropic regularizatio
Externí odkaz:
http://arxiv.org/abs/2410.03353
In optimal transport, quadratic regularization is a sparse alternative to entropic regularization: the solution measure tends to have small support. Computational experience suggests that the support decreases monotonically to the unregularized count
Externí odkaz:
http://arxiv.org/abs/2408.07871
Autor:
González-Sanz, Alberto, Sheng, Shunan
Optimal transport has proven to have numerous applications in data science. In some of those, differentiating the transport map with respect to the densities in the Fr\'echet sense is required. In this work, we prove that for a compact, uniformly con
Externí odkaz:
http://arxiv.org/abs/2408.06534
Autor:
González-Sanz, Alberto, Nutz, Marcel
Linear programs with quadratic regularization are attracting renewed interest due to their applications in optimal transport: unlike entropic regularization, the squared-norm penalty gives rise to sparse approximations of optimal transport couplings.
Externí odkaz:
http://arxiv.org/abs/2408.04088
The quadratically regularized optimal transport problem has recently been considered in various applications where the coupling needs to be \emph{sparse}, i.e., the density of the coupling needs to be zero for a large subset of the product of the sup
Externí odkaz:
http://arxiv.org/abs/2407.21528
The push-forward operation enables one to redistribute a probability measure through a deterministic map. It plays a key role in statistics and optimization: many learning problems (notably from optimal transport, generative modeling, and algorithmic
Externí odkaz:
http://arxiv.org/abs/2403.07471
Autor:
Mirabadi, Ali Khajegili, Archibald, Graham, Darbandsari, Amirali, Contreras-Sanz, Alberto, Nakhli, Ramin Ebrahim, Asadi, Maryam, Zhang, Allen, Gilks, C. Blake, Black, Peter, Wang, Gang, Farahani, Hossein, Bashashati, Ali
Cancer subtyping is one of the most challenging tasks in digital pathology, where Multiple Instance Learning (MIL) by processing gigapixel whole slide images (WSIs) has been in the spotlight of recent research. However, MIL approaches do not take adv
Externí odkaz:
http://arxiv.org/abs/2402.03592
Autor:
Antonio Santos Ortega
Publikováno v:
Cuadernos de Relaciones Laborales, Vol 39, Iss 1 (2021)
Externí odkaz:
https://doaj.org/article/0389a225c25d4db8879c24d44366b2c8
The inverse optimal transport problem is to find the underlying cost function from the knowledge of optimal transport plans. While this amounts to solving a linear inverse problem, in this work we will be concerned with the nonlinear inverse problem
Externí odkaz:
http://arxiv.org/abs/2312.05843
The distribution regression problem encompasses many important statistics and machine learning tasks, and arises in a large range of applications. Among various existing approaches to tackle this problem, kernel methods have become a method of choice
Externí odkaz:
http://arxiv.org/abs/2308.14335