Add to ORKGIn recent years, there has been a significant growth in research focusing on minimum $\ell_2$ norm (ridgeless) interpolation least squares estimators. However, the majority of these analyses have been limited to a simple regression error structure, assuming independent and identically distributed errors with zero mean and common variance. In this paper, we explore prediction risk as well as estimation risk under more general regression error assumptions, highlighting the benefits of overparameterization in a finite sample. We find that including a large number of unimportant parameters relative to the sample size can effectively reduce both risks. Notably, we establish that the estimation difficulties associated with the variance components...
Bagging is a commonly used ensemble technique in statistics and machine learning to improve the perf...
Statistical Learning Theory studies the problem of learning an unknown relationship between observed...
In this study, the techniques of ridge regression model as alternative to the classical ordinary lea...
Interpolators -- estimators that achieve zero training error -- have attracted growing attention in ...
We analyze the prediction error of ridge re- gression in an asymptotic regime where the sample size ...
Overparametrization often helps improve the generalization performance. This paper presents a dual v...
In the linear regression model, the minimum l2-norm interpolant estimator has received much attentio...
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one ...
In this note, we provide an elementary analysis of the prediction error of ridge regression with ran...
In this note, we provide an elementary analysis of the prediction error of ridge regression with ran...
We examine the necessity of interpolation in overparameterized models, that is, when achieving optim...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...
A Two-Stage approach is described that literally "straighten outs" any potentially nonlinear relatio...
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one ...
Bagging is a commonly used ensemble technique in statistics and machine learning to improve the perf...
Bagging is a commonly used ensemble technique in statistics and machine learning to improve the perf...
Statistical Learning Theory studies the problem of learning an unknown relationship between observed...
In this study, the techniques of ridge regression model as alternative to the classical ordinary lea...
Interpolators -- estimators that achieve zero training error -- have attracted growing attention in ...
We analyze the prediction error of ridge re- gression in an asymptotic regime where the sample size ...
Overparametrization often helps improve the generalization performance. This paper presents a dual v...
In the linear regression model, the minimum l2-norm interpolant estimator has received much attentio...
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one ...
In this note, we provide an elementary analysis of the prediction error of ridge regression with ran...
In this note, we provide an elementary analysis of the prediction error of ridge regression with ran...
We examine the necessity of interpolation in overparameterized models, that is, when achieving optim...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...
A Two-Stage approach is described that literally "straighten outs" any potentially nonlinear relatio...
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one ...
Bagging is a commonly used ensemble technique in statistics and machine learning to improve the perf...
Bagging is a commonly used ensemble technique in statistics and machine learning to improve the perf...
Statistical Learning Theory studies the problem of learning an unknown relationship between observed...
In this study, the techniques of ridge regression model as alternative to the classical ordinary lea...