Copulas in Machine Learning
Gal Elidan
- 01 Jan 2013
- pp 39-60
TL;DR: The purpose of this paper is to survey recent copula-based constructions in the field of machine learning so as to provide a stepping stone for those interested in further exploring this emerging symbiotic research.
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Abstract: Despite overlapping goals of multivariate modeling and dependence identification, until recently the fields of machine learning in general and probabilistic graphical models in particular have been ignorant of the framework of copulas At the same time, the complementing strengths of the two fields suggests the great fruitfulness of a synergy The purpose of this paper is to survey recent copula-based constructions in the field of machine learning, so as to provide a stepping stone for those interested in further exploring this emerging symbiotic research
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Figures
Table 1 Summary of the different copula-based multivariate models 
Fig. 2 An toy Bayesian network of a Mars relocation scenario where f (·) = f (H) f (S) f (E|S,H) f (M|S) f (R|E,M). 
Fig. 1 Samples from the bivariate Gaussian copula with correlation θ = 0.25. (left) with unit variance Gaussian and Gamma marginals; (right) with a mixture of Gaussian and exponential marginals.
Citations
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Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios,Eric Nalisnick,Danilo Jimenez Rezende,Shakir Mohamed,Balaji Lakshminarayanan +4 more
TL;DR: This review places special emphasis on the fundamental principles of flow design, and discusses foundational topics such as expressive power and computational trade-offs, and summarizes the use of flows for tasks such as generative modeling, approximate inference, and supervised learning.
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Neural Spline Flows
Conor Durkan,Artur Bekasov,Iain Murray,George Papamakarios +3 more
- 14 Dec 2019
TL;DR: The authors proposed a fully differentiable module based on monotonic rational-quadratic splines, which enhances the flexibility of both coupling and autoregressive transforms while retaining analytic invertibility.
•Journal Article
Multivariate Spearman's rho for Aggregating Ranks Using Copulas
Justin Bedo,Cheng Soon Ong +1 more
TL;DR: The main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's ρ, which measures correlation between a set of ranked lists.
Multivariate spearman's ρ for aggregating ranks using copulas
Justin Bedo,Cheng Soon Ong +1 more
TL;DR: In this paper, a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's ρ is proposed, which measures correlation between a set of ranked lists.
358
•Posted Content
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
TL;DR: This work proposes to combine an RNN-based time series model with a Gaussian copula process output model withA low-rank covariance structure to reduce the computational complexity and handle non-Gaussian marginal distributions.
150
References
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
Gideon Schwarz
- 01 Jan 2005
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
40.6K
•Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
- 01 Jan 1993
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
An Introduction to the Bootstrap.
Bradley Efron,Robert Tibshirani +1 more
TL;DR: In this article, the authors present a geometric representation for the Bootstrap and the Jackknife, as well as an overview of nonparametric and Parametric Inference methods for estimating the error in Bootstrap estimates.
15.3K