
On Affine and Conjugate Nonparametric Regression
Suppose the nonparametric regression function of a response variable Y o...
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The linear conditional expectation in Hilbert space
The linear conditional expectation (LCE) provides a best linear (or rath...
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Conditional Expectation Propagation
Expectation propagation (EP) is a powerful approximate inference algorit...
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Normalizing Flow Regression
In this letter we propose a convex approach to learning expressive scala...
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The Undecidability of Conditional Affine Information Inequalities and Conditional Independence Implication with a Binary Constraint
We establish the undecidability of conditional affine information inequa...
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Modelling bidask spread conditional distributions using hierarchical correlation reconstruction
While we would like to predict exact values, available incomplete inform...
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Dimensionfree PACBayesian bounds for matrices, vectors, and linear least squares regression
This paper is focused on dimensionfree PACBayesian bounds, under weak ...
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On Least Squares Linear Regression Without Second Moment
If X and Y are real valued random variables such that the first moments of X, Y, and XY exist and the conditional expectation of Y given X is an affine function of X, then the intercept and slope of the conditional expectation equal the intercept and slope of the least squares linear regression function, even though Y may not have a finite second moment. As a consequence, the affine in X form of the conditional expectation and zero covariance imply mean independence.
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