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pro vyhledávání: '"k-fold"'
Autor:
Alon, Noga, Gravin, Nick, Pollner, Tristan, Rubinstein, Aviad, Wang, Hongao, Weinberg, S. Matthew, Zhang, Qianfan
We investigate prophet inequalities with competitive ratios approaching $1$, seeking to generalize $k$-uniform matroids. We first show that large girth does not suffice: for all $k$, there exists a matroid of girth $\geq k$ and a prophet inequality i
Externí odkaz:
http://arxiv.org/abs/2411.11741
Autor:
Kislay, Kaustubh, Singh, Shlok, Joshi, Soham, Dutta, Rohan, Flint, Jay Shim George, Zhu, Kevin
Symbolic Regression remains an NP-Hard problem, with extensive research focusing on AI models for this task. Transformer models have shown promise in Symbolic Regression, but performance suffers with smaller datasets. We propose applying k-fold cross
Externí odkaz:
http://arxiv.org/abs/2410.21896
Autor:
Hocking, Toby Dylan, Thibault, Gabrielle, Bodine, Cameron Scott, Arellano, Paul Nelson, Shenkin, Alexander F, Lindly, Olivia Jasmine
In many real-world applications of machine learning, we are interested to know if it is possible to train on the data that we have gathered so far, and obtain accurate predictions on a new test data subset that is qualitatively different in some resp
Externí odkaz:
http://arxiv.org/abs/2410.08643
This research aims to propose and evaluate a novel model named K-Fold Causal Bayesian Additive Regression Trees (K-Fold Causal BART) for improved estimation of Average Treatment Effects (ATE) and Conditional Average Treatment Effects (CATE). The stud
Externí odkaz:
http://arxiv.org/abs/2409.05665
Autor:
Brodskiy, Michael, Howell, Owen L.
Random Matrix Theory is a powerful tool in applied mathematics. Three canonical models of random matrix distributions are the Gaussian Orthogonal, Unitary and Symplectic Ensembles. For matrix ensembles defined on k-fold tensor products of identical v
Externí odkaz:
http://arxiv.org/abs/2405.01727
As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference. K-fold cross-validation (CV) is the most common approach to ascertaining the likelihood that a machine learning outcome
Externí odkaz:
http://arxiv.org/abs/2401.16407
Autor:
Fazekas, Attila, Kovacs, Gyorgy
K-fold cross-validation is a widely used tool for assessing classifier performance. The reproducibility crisis faced by artificial intelligence partly results from the irreproducibility of reported k-fold cross-validation-based performance scores. Re
Externí odkaz:
http://arxiv.org/abs/2401.13843
Akademický článek
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Autor:
Linnenbrink, Jan1 (AUTHOR) jan.linnenbrink@uni-muenster.de, Milà, Carles2,3 (AUTHOR), Ludwig, Marvin1 (AUTHOR), Meyer, Hanna1 (AUTHOR)
Publikováno v:
Geoscientific Model Development. 2024, Vol. 17 Issue 15, p5897-5912. 16p.
Akademický článek
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