… We consider the shuffled linear regression problem where the correspondences between …
Therefore, in this work, we generalize the formulation of shuffled linear regression to a broader …

… Figure 1: We study shuffled linear regression, the problem of estimating linear weights
w0 given … Does this assumption restrict our ability to generalize shuffled regression to non-linear …

… As a variant of ordinary linear regression, it requires estimating not only the regression …
In this work, we generalize the formulation of shuffled linear regression to a broader range …

… Overall, for n ≤ 5, our approach is the first working solution to shuffled linear regression that
remains … Definition 1 is a generalization of the notion of dimension in linear algebra: if Y is a …

… Recently, there has been significant interest in linear regression in the … of regression
parameters. In this article, we present a method to adjust for such mismatches under “partial shuffling…

… Shuffled regression is the problem of learning regression models from shuffled data that …
new deep learning method for shuffled regression called Shuffled Deep Regression (SDR). …

… 1This paper considers the task of linear regression with shuffled … linear regression arises as
a natural generalization. … Another potential application of permuted linear regression is in the …

… problem, which paves way for our work in studying the permuted linear regression problem.
… dimensional ridge-regularized generalized robust regression estimators. Probability Theory …

We conduct Monte Carlo experiments to study the statistical efficiencies of the 50%-trimming
operator and the Huber-skip estimator on the least trimmed square problem. We follow the …

… structures such as sparse and local shuffling. This prior turns out to be conjugate for canonical
shuffled data problems in which the … Specifically, we consider the linear regression setup …