Zobrazeno 1 - 10
of 621
pro vyhledávání: '"Sanz, Alberto"'
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
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
The contribution of this work is twofold. The first part deals with a Hilbert-space version of McCann's celebrated result on the existence and uniqueness of monotone measure-preserving maps: given two probability measures $\rm P$ and $\rm Q$ on a sep
Externí odkaz:
http://arxiv.org/abs/2305.11751
We establish weak limits for the empirical entropy regularized optimal transport cost, the expectation of the empirical plan and the conditional expectation. Our results require only uniform boundedness of the cost function and no smoothness properti
Externí odkaz:
http://arxiv.org/abs/2305.09745
For a probability P in $R^d$ its center outward distribution function $F_{\pm}$, introduced in Chernozhukov et al. (2017) and Hallin et al. (2021), is a new and successful concept of multivariate distribution function based on mass transportation the
Externí odkaz:
http://arxiv.org/abs/2303.16862
We present a novel kernel over the space of probability measures based on the dual formulation of optimal regularized transport. We propose an Hilbertian embedding of the space of probabilities using their Sinkhorn potentials, which are solutions of
Externí odkaz:
http://arxiv.org/abs/2210.06574
This work deals with the asymptotic distribution of both potentials and couplings of entropic regularized optimal transport for compactly supported probabilities in $\R^d$. We first provide the central limit theorem of the Sinkhorn potentials -- the
Externí odkaz:
http://arxiv.org/abs/2207.07427
The diffeomorphic registration framework enables to define an optimal matching function between two probability measures with respect to a data-fidelity loss function. The non convexity of the optimization problem renders the choice of this loss func
Externí odkaz:
http://arxiv.org/abs/2206.13948