Zobrazeno 1 - 10
of 779
pro vyhledávání: '"Tabak, A. G."'
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
Autor:
Fazekas-Pongor, Vince, Domján, Beatrix A., Major, Dávid, Péterfi, Anna, Horváth, Viktor J., Mészáros, Szilvia, Vokó, Zoltán, Vásárhelyi, Barna, Szabó, Attila J, Burián, Katalin, Merkely, Béla, Tabák, Adam G.
Publikováno v:
In Diabetes Research and Clinical Practice October 2024 216
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:
Tabak, Adam G.1,2,3 (AUTHOR) a.tabak@ucl.ac.uk, Kempler, Peter1 (AUTHOR), Guja, Cristian4 (AUTHOR), Eldor, Roy5,6 (AUTHOR), Haluzik, Martin7 (AUTHOR), Klupa, Tomasz8 (AUTHOR), Papanas, Nikolaos9 (AUTHOR), Stoian, Anca Pantea4 (AUTHOR), Mankovsky, Boris10 (AUTHOR)
Publikováno v:
Diabetes Therapy. May2024, Vol. 15 Issue 5, p897-915. 19p.
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
Akademický článek
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Autor:
Tabak, Rachel G., author, Nilsen, Per, author, Woodward, Eva N., author, Chambers, David A., author
Publikováno v:
Dissemination and Implementation Research in Health : Translating Science to Practice, 2023, ill.
Externí odkaz:
https://doi.org/10.1093/oso/9780197660690.003.0004
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