Zobrazeno 1 - 10
of 406
pro vyhledávání: '"Rosset, Saharon"'
Autor:
Yuval, Oren, Rosset, Saharon
We present a methodology for using unlabeled data to design semi supervised learning (SSL) methods that improve the prediction performance of supervised learning for regression tasks. The main idea is to design different mechanisms for integrating th
Externí odkaz:
http://arxiv.org/abs/2302.09526
Efron's two-group model is widely used in large scale multiple testing. This model assumes that test statistics are mutually independent, however in realistic settings they are typically dependent, and taking the dependence into account can boost pow
Externí odkaz:
http://arxiv.org/abs/2212.08856
Autor:
Simchoni, Giora, Rosset, Saharon
Modern approaches to supervised learning like deep neural networks (DNNs) typically implicitly assume that observed responses are statistically independent. In contrast, correlated data are prevalent in real-life large-scale applications, with typica
Externí odkaz:
http://arxiv.org/abs/2206.03314
Autor:
Pazokitoroudi, Ali, Liu, Zhengtong, Dahl, Andrew, Zaitlen, Noah, Rosset, Saharon, Sankararaman, Sriram
Publikováno v:
In The American Journal of Human Genetics 11 July 2024 111(7):1462-1480
Autor:
Rabinowicz, Assaf, Rosset, Saharon
This paper proposes parametric and non-parametric hypothesis testing algorithms for detecting anisotropy -- rotational variance of the covariance function in random fields. Both algorithms are based on resampling mechanisms, which enable avoiding rel
Externí odkaz:
http://arxiv.org/abs/2105.01654
Publikováno v:
Biometrics, 2022
A central goal in designing clinical trials is to find the test that maximizes power (or equivalently minimizes required sample size) for finding a false null hypothesis subject to the constraint of type I error. When there is more than one test, suc
Externí odkaz:
http://arxiv.org/abs/2104.01346
Autor:
Rabinowicz, Assaf, Rosset, Saharon
This paper presents a new approach for trees-based regression, such as simple regression tree, random forest and gradient boosting, in settings involving correlated data. We show the problems that arise when implementing standard trees-based regressi
Externí odkaz:
http://arxiv.org/abs/2102.08114
Autor:
Yuval, Oren, Rosset, Saharon
We present a general methodology for using unlabeled data to design semi supervised learning (SSL) variants of the Empirical Risk Minimization (ERM) learning process. Focusing on generalized linear regression, we analyze of the effectiveness of our S
Externí odkaz:
http://arxiv.org/abs/2009.00606
Autor:
Rabinowicz, Assaf, Rosset, Saharon
K-fold cross-validation (CV) with squared error loss is widely used for evaluating predictive models, especially when strong distributional assumptions cannot be taken. However, CV with squared error loss is not free from distributional assumptions,
Externí odkaz:
http://arxiv.org/abs/1904.02438
Interpolators -- estimators that achieve zero training error -- have attracted growing attention in machine learning, mainly because state-of-the art neural networks appear to be models of this type. In this paper, we study minimum $\ell_2$ norm ("ri
Externí odkaz:
http://arxiv.org/abs/1903.08560