arXiv research

StaPLR selects important views for multi-view learning models.

Study evaluates different meta-learners for multi-view stacking.

Combines response categories in multinomial logistic regression models.

Sparse multinomial logistic regression for multiclass classification with feature selection.

FILTER model uses fusion penalized logistic threshold regression for high-dimensional data with unknown cut points.

Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for releasing GWAS data in a privacy-preserving way. Here, we develop an end-to-end differentially private method for solving regression problems wi…

We develop a first order expansion for convex penalized estimators in high-dimensional regression.

The paper explores MMPR to select diverse models for scientific insight.

Enhances privacy-preserving logistic regression for diabetes prediction.

New method calibrates logistic regression tuning parameters.

A Qini-based uplift model improves retention marketing campaign performance.

New method for imputing missing data in multi-view data.

Proposes a model selection procedure for high-dimensional binary classification using sparse logistic regression.

We propose a new algorithm called PLUTO for building logistic regression trees to binary response data. PLUTO can capture the nonlinear and interaction patterns in messy data by recursively partitioning the sample space. It fits a simple or a multiple linear logistic regression model in each partition. PLUTO employs th…

High-dimensional feature selection arises in many areas of modern science. For example, in genomic research we want to find the genes that can be used to separate tissues of different classes (e.g. cancer and normal) from tens of thousands of genes that are active (expressed) in certain tissue cells. To this end, we wi…

Paper shows noisy labels can improve PLR variable selection.

New method for multiclass classification reduces error bounds.

Machine learning predicts Bitcoin price with high accuracy.

A new learning method using hyperbolic geometry for class labels.

Softplus regressions use multiple hyperplanes to classify data.

New weighted Lasso estimates improve logistic regression performance with measurement error.

StaPLR improves Alzheimer's disease classification by identifying important MRI scan types and measures.

A new method speeds up learning sparse binary networks.

Improved logistic regression for multi-omics data improves prediction and variable selection.

The article compares neural networks and logistic regression for credit scoring and introduces a new probability calibration technique.

Improved CRT for sparse logistic regression in high dimensions.

The $\ell_1$-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weigh…

New method predicts customer churn using mixed-penalty logistic regression.

Deep learning detects genetic interactions in type 2 diabetes.

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamenta…

Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has been proposed as a way of extending the ideas of the lasso to the problem of group…

The logistic regression model is known to converge to a Poisson point process model if the binary response tends to infinitely imbalanced. In this paper, it is shown that this phenomenon is universal in a wide class of link functions on binomial regression. The proof relies on the extreme value theory. For the logit, p…

MetaStackVis aids in choosing better metamodels for stacking ensembles.

We investigate a robust penalized logistic regression algorithm based on a minimum distance criterion. Influential outliers are often associated with the explosion of parameter vector estimates, but in the context of standard logistic regression, the bias due to outliers always causes the parameter vector to implode, t…

Improved Frank-Wolfe algorithm speeds up training of differentially private LASSO models.

Deep learning predicts asthma ED visits better than traditional methods.

The paper forecasts corporate distress using a novel MIDAS logistic regression method.

NN-Stacking improves predictive power of regression models by adjusting stacking coefficients with features.

Logistic regression connects to perceptron learning via gradient ascent.

New lower bounds improve logistic log-likelihood optimization and inference.

Improved asset allocation strategies using penalized quantile regression.

Paper finds a lower bound for estimating low-rank matrices in logistic regression.

Develops a tool to identify abnormal blood smear results based on CBC tests.

Stacked regressions improve predictive accuracy by combining estimators.

We consider the problem of estimating the topology of spatial interactions in a discrete state, discrete time spatio-temporal graphical model where the interactions affect the temporal evolution of each agent in a network. Among other models, the susceptible, infected, recovered ($SIR$) model for interaction events fal…

The paper uses machine learning to select stocks based on features and algorithms.

Novel bounds for logistic regression coreset construction and feature selection.

Unified framework for sparse logistic regression with nonconvex regularization.