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ACM/IEEE Joint Conference on Digital Libraries…
Citations implicitly encode a community's judgment of a paper's importance and thus provide a unique signal by which to study scientific impact. Efforts in understanding and refining this signal are… Continue Reading
In an extension of Kendall’s $$\tau $$τ, Bergsma and Dassios (Bernoulli 20(2):1006–1028, 2014) introduced a covariance measure $$\tau ^*$$τ∗ for two ordinal random variables that vanishes if and only… Continue Reading
Abstract Linear structural equation models relate the components of a random vector using linear interdependencies and Gaussian noise. Each such model can be naturally associated with a mixed graph… Continue Reading
Linear structural equation models, which relate random variables via linear interdependencies and Gaussian noise, are a popular tool for modelling multivariate joint distributions. The models… Continue Reading
IEEE/CVF Conference on Computer Vision and…
Collaboration is a necessary skill to perform tasks that are beyond one agent's capabilities. Addressed extensively in both conventional and modern AI, multi-agent collaboration has often been… Continue Reading
The need to test whether two random vectors are independent has spawned a large number of competing measures of dependence. We are interested in nonparametric measures that are invariant under… Continue Reading
Gaussian latent tree models, or more generally, Gaussian latent forest models have Fisher-information matrices that become singular along interesting submodels, namely, models that correspond to… Continue Reading
The Bergsma-Dassios sign covariance is a recently proposed extension of Kendall's tau. In contrast to tau or also Spearman's rho, the new sign covariance $\tau^*$ vanishes if and only if the two… Continue Reading
A comprehensive and up-to-date analysis of Computer Science literature (2.87 million papers through 2018) reveals that, if current trends continue, parity between the number of male and female… Continue Reading