A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Optimizes hyperparameter tuning for models using approximate leave-one-out cross-validation.
The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validation. In this article, we consider Gaussian latent variable models where the integration over the latent values is approximated using the Laplace method or expectation propagation (EP). We study the properties of several B…
Improved LOO cross-validation for function approximation.
Optimizes Lasso hyperparameters using leave-one-out CV.
We develop an approximate formula for evaluating a cross-validation estimator of predictive likelihood for multinomial logistic regression regularized by an -norm. This allows us to avoid repeated optimizations required for literally conducting cross-validation; hence, the computational time can be significantl…
Cross validation residuals are well known for the ordinary least squares model. Here leave-M-out cross validation is extended to generalised least squares. The relationship between cross validation residuals and Cook's distance is demonstrated, in terms of an approximation to the difference in the generalised residual …
New method approximates CV for model assessment and selection.
ACV improves structured data CV, handling dependence and noisy initial fits.
New method approximates CV efficiently for large-scale problems.
ALO-CV approximates leave-one-out error in proportional regime.
Cross-validation estimates model performance on unseen data, not training data.
The paper assesses quality measures for machine learning models using cross-validation.
New ACV method speeds up CV in high dimensions with approximate low-rank data.
Paper analyzes holdout cross-validation for large non-Gaussian covariance estimation.
The paper improves ALO for -regularized models.
A method for efficient CV estimates in Bayesian hierarchical models.
Leave-one-out cross-validation (LOOCV) can be particularly accurate among cross-validation (CV) variants for machine learning assessment tasks -- e.g., assessing methods' error or variability. But it is expensive to re-fit a model times for a dataset of size . Previous work has shown that approximations to LOOCV…
Cross-validation (CV) is a technique for evaluating the ability of statistical models/learning systems based on a given data set. Despite its wide applicability, the rather heavy computational cost can prevent its use as the system size grows. To resolve this difficulty in the case of Bayesian linear regression, we dev…
Improved GP decoder training with SAS approximations.
Paper introduces NICc for fast cluster-based validation of prediction models.
Manifold regularization, such as laplacian regularized least squares (LapRLS) and laplacian support vector machine (LapSVM), has been widely used in semi-supervised learning, and its performance greatly depends on the choice of some hyper-parameters. Cross-validation (CV) is the most popular approach for selecting the …
Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO) is a general approach for assessing the generalizability of a model, but unfortunately, LOO does not scale well to large datasets. We propose a combination of u…
Optimal data splitting improves covariance matrix estimation in large datasets.
MuyGPs efficiently estimates GP hyperparameters using local cross-validation.
NoMoPy models noise as HMM/FHMM in Python.
Risk estimation is at the core of many learning systems. The importance of this problem has motivated researchers to propose different schemes, such as cross validation, generalized cross validation, and Bootstrap. The theoretical properties of such estimates have been extensively studied in the low-dimensional setting…
The correct use of model evaluation, model selection, and algorithm selection techniques is vital in academic machine learning research as well as in many industrial settings. This article reviews different techniques that can be used for each of these three subtasks and discusses the main advantages and disadvantages …
Method learns SDEs from one trajectory using GP priors and randomized cross-validation.
In this article, we derive concentration inequalities for the cross-validation estimate of the generalization error for empirical risk minimizers. In the general setting, we prove sanity-check bounds in the spirit of \cite{KR99} \textquotedblleft\textit{bounds showing that the worst-case error of this estimate is not m…
Efficiently estimates shrinkage coefficient for RTME using LOOCV approximation.
This work improves AutoML systems by dynamically evaluating fitness to reduce overfitting.
In this article, we derive concentration inequalities for the cross-validation estimate of the generalization error for stable predictors in the context of risk assessment. The notion of stability has been first introduced by \cite{DEWA79} and extended by \cite{KEA95}, \cite{BE01} and \cite{KUNIY02} to characterize cla…
In this paper, we propose a low-rank approximation method based on discrete least-squares for the approximation of a multivariate function from random, noisy-free observations. Sparsity inducing regularization techniques are used within classical algorithms for low-rank approximation in order to exploit the possible sp…
In this article, we derive concentration inequalities for the cross-validation estimate of the generalization error for subagged estimators, both for classification and regressor. General loss functions and class of predictors with both finite and infinite VC-dimension are considered. We slightly generalize the formali…
New algorithms delete user data from machine learning models efficiently.
When selecting a classification algorithm to be applied to a particular problem, one has to simultaneously select the best algorithm for that dataset \emph{and} the best set of hyperparameters for the chosen model. The usual approach is to apply a nested cross-validation procedure; hyperparameter selection is performed…
A fast bootstrap method estimates cross-validation standard error.
New cross-validation methods for Gaussian process regression with efficient gradient computation.
We investigate the signal reconstruction performance of sparse linear regression in the presence of noise when piecewise continuous nonconvex penalties are used. Among such penalties, we focus on the SCAD penalty. The contributions of this study are three-fold: We first present a theoretical analysis of a typical recon…
Improves test set performance and reduces out-of-sample disappointment for unstable models.
The paper develops a cross-validation method for improving signal denoising techniques.
Study evaluates cross-validation methods for clinical ECG classification, finding leave-source-out more reliable.
A new method improves super learner validation efficiency.
We consider the parametric learning problem, where the objective of the learner is determined by a parametric loss function. Employing empirical risk minimization with possibly regularization, the inferred parameter vector will be biased toward the training samples. Such bias is measured by the cross validation procedu…
Cross-validation is the workhorse of modern applied statistics and machine learning, as it provides a principled framework for selecting the model that maximizes generalization performance. In this paper, we show that the cross-validation risk is differentiable with respect to the hyperparameters and training data for …
Cross-validation methods help learn dynamical systems from data.
This text is a survey on cross-validation. We define all classical cross-validation procedures, and we study their properties for two different goals: estimating the risk of a given estimator, and selecting the best estimator among a given family. For the risk estimation problem, we compute the bias (which can also be …
The paper improves confidence intervals for test error using cross-validation.