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pro vyhledávání: '"Y., Samuel"'
Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal model or a single equivalence class of models, less attention has been g
Externí odkaz:
http://arxiv.org/abs/2305.14506
Publikováno v:
International Conference on Machine Learning 2021
We propose a residual randomization procedure designed for robust Lasso-based inference in the high-dimensional setting. Compared to earlier work that focuses on sub-Gaussian errors, the proposed procedure is designed to work robustly in settings tha
Externí odkaz:
http://arxiv.org/abs/2106.07717
Undirected graphical models are widely used to model the conditional independence structure of vector-valued data. However, in many modern applications, for example those involving EEG and fMRI data, observations are more appropriately modeled as mul
Externí odkaz:
http://arxiv.org/abs/2105.02487
Autor:
Wang, Y. Samuel, Drton, Mathias
We consider recovering causal structure from multivariate observational data. We assume the data arise from a linear structural equation model (SEM) in which the idiosyncratic errors are allowed to be dependent in order to capture possible latent con
Externí odkaz:
http://arxiv.org/abs/2007.11131
We consider the problem of estimating the difference between two undirected functional graphical models with shared structures. In many applications, data are naturally regarded as a vector of random functions rather than as a vector of scalars. For
Externí odkaz:
http://arxiv.org/abs/2003.05402
We consider the problem of estimating the difference between two functional undirected graphical models with shared structures. In many applications, data are naturally regarded as high-dimensional random function vectors rather than multivariate sca
Externí odkaz:
http://arxiv.org/abs/1910.09701
In this article, we investigate the role of gender in collaboration patterns by analyzing gender-based homophily -- the tendency for researchers to co-author with individuals of the same gender. We develop and apply novel methodology to the corpus of
Externí odkaz:
http://arxiv.org/abs/1909.01284
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In silico design of new molecules and materials with desirable quantum properties by high-throughput screening is a major challenge due to the high dimensionality of chemical space. To facilitate its navigation, we present a unification of coordinate
Externí odkaz:
http://arxiv.org/abs/1809.03302
Prior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variances. We show that this fact is implied by an ordering among (conditional) v
Externí odkaz:
http://arxiv.org/abs/1807.03419