Then, we study the performance of regression-based CI tests under misspecified inductive biases.

The Gaussianity assumption has been consistently criticized as a main limitation of the V ariational Autoencoder (V AE) despite its efficiency in computational modeling. In this paper, we propose a new approach that expands the model capacity (i.e., expressive power of distributional family) without sacrificing the computational advantages of the V AE framework. Our V AE model's decoder is composed of an infinite mixture of asymmetric Laplace distribution, which possesses general

Distribution shifts in multi-dimensional data, caused by one or more "corrupted" dimensions, are

Evaluated over a collection of 25 linear regression datasets taken from Tang et al.

Previous work has focused primarily on developing ML-based "meta-learners" that can provide point estimates of the conditional

This paper studies quasi-Newton methods for solving nonlinear equations. We propose block variants of both good and bad Broyden's methods, which enjoy explicit local superlinear convergence rates. Our block good Broyden's method has a faster condition-number-free convergence rate than existing Broyden's methods because it takes the advantage of multiple rank modification on Jacobian estimator. On the other hand, our block bad Broyden's method directly estimates the inverse of the Jacobian provably, which reduces the computational cost of the iteration. Our theoretical results provide some new insights on why good Broyden's method outperforms bad Broyden's method in most of the cases. The empirical results also demonstrate the superiority of our methods and validate our theoretical analysis.

A vast majority of prior work on robustness however assumes data homogeneity, i.e., local datasets are generated from the same distribution.