Zobrazeno 1 - 10
of 173
pro vyhledávání: '"Tabak, Esteban"'
This article develops a novel data assimilation methodology, addressing challenges that are common in real-world settings, such as severe sparsity of observations, lack of reliable models, and non-stationarity of the system dynamics. These challenges
Externí odkaz:
http://arxiv.org/abs/2411.01786
A new method is proposed for the solution of the data-driven optimal transport barycenter problem and of the more general distributional barycenter problem that the article introduces. The method improves on previous approaches based on adversarial g
Externí odkaz:
http://arxiv.org/abs/2104.14329
Autor:
Kim, Daeyoung, Tabak, Esteban G.
A novel algorithm is proposed to solve the sample-based optimal transport problem. An adversarial formulation of the push-forward condition uses a test function built as a convolution between an adaptive kernel and an evolving probability distributio
Externí odkaz:
http://arxiv.org/abs/2006.04245
Publikováno v:
Journal of Biomedical Informatics. 113 (2021) 103639
Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition falls short, but not all ML-generated information is of equal utility for
Externí odkaz:
http://arxiv.org/abs/1911.09856
Autor:
Yang, Hongkang, Tabak, Esteban G.
A framework is proposed that addresses both conditional density estimation and latent variable discovery. The objective function maximizes explanation of variability in the data, achieved through the optimal transport barycenter generalized to a coll
Externí odkaz:
http://arxiv.org/abs/1910.14090
A data driven procedure is developed to compute the optimal map between two conditional probabilities $\rho(x|z_{1},...,z_{L})$ and $\mu(y|z_{1},...,z_{L})$ depending on a set of covariates $z_{i}$. The procedure is tested on synthetic data from the
Externí odkaz:
http://arxiv.org/abs/1910.11422
A game theory inspired methodology is proposed for finding a function's saddle points. While explicit descent methods are known to have severe convergence issues, implicit methods are natural in an adversarial setting, as they take the other player's
Externí odkaz:
http://arxiv.org/abs/1906.00233
Autor:
Liu, Sheng, Cheng, Mark, Brooks, Hayley, Mackey, Wayne, Heeger, David J., Tabak, Esteban G., Fernandez-Granda, Carlos
We propose a nonparametric model for time series with missing data based on low-rank matrix factorization. The model expresses each instance in a set of time series as a linear combination of a small number of shared basis functions. Constraining the
Externí odkaz:
http://arxiv.org/abs/1904.04780
Autor:
Yang, Hongkang, Tabak, Esteban G.
The clustering problem, and more generally, latent factor discovery --or latent space inference-- is formulated in terms of the Wasserstein barycenter problem from optimal transport. The objective proposed is the maximization of the variability attri
Externí odkaz:
http://arxiv.org/abs/1902.10288
An adaptive, adversarial methodology is developed for the optimal transport problem between two distributions $\mu$ and $\nu$, known only through a finite set of independent samples $(x_i)_{i=1..N}$ and $(y_j)_{j=1..M}$. The methodology automatically
Externí odkaz:
http://arxiv.org/abs/1807.00393