arXiv research

Maximum likelihood estimator performance in logistic regression analyzed.

Proposes logistic-beta process for modeling dependent probabilities with beta marginals.

Classification is the most important process in data analysis. However, due to the inherent non-convex and non-smooth structure of the zero-one loss function of the classification model, various convex surrogate loss functions such as hinge loss, squared hinge loss, logistic loss, and exponential loss are introduced. T…

Novel bounds for logistic regression coreset construction and feature selection.

Improved confidence bounds for linear logistic model with applications to bandits.

Paper introduces imprecise logistic regression for handling uncertain data.

Improved regret bounds for logistic bandits via novel confidence set construction.

Prevalidated ridge regression simplifies logistic regression for high-dimensional data.

Study shows sample complexity for logistic regression with normal covariates.

Estimates future ICU demand using age-structured data and logistic modeling.

Improved classifier for PU data using logistic regression.

In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition Bosch Production Line Performance. We considered the use of machine learning, linear and Bayesian models. For machine learning approach, we analyzed XGBoost…

Improves logistic regression performance with nonconvex programming.

PbP strategy improves logistic model prediction with missing values.

Proposes MELODIC family for simultaneous binary logistic regression.

Develops first optimal algorithm for logistic bandits.

The thesis examines stochastic calculus in option pricing with logistic models and numerical methods.

Multilabel classification is an important problem in a wide range of domains such as text categorization and music annotation. In this paper, we present a probabilistic model, Multilabel Logistic Regression with Hidden variables (MLRH), which extends the standard logistic regression by introducing hidden variables. Hid…

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…

New framework optimizes classification trees with logistic loss and $\ell_1$ regularization.

A new algorithm approximates logistic regression probabilities efficiently.

A new approach for signal parametrization, which consists of a specific regression model incorporating a discrete hidden logistic process, is proposed. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The parameters of the hidden logis…

New method tackles parcel routing with AI.

We analyze the optimization landscape of α-loss in logistic models.

Paper explores connections between loss functions and consistency in binary classification and regression.

Sparse multinomial logistic regression for multiclass classification with feature selection.

Machine learning improves joint default assessment by capturing non-linear dependencies.

$L_1$ regularized logistic regression has now become a workhorse of data mining and bioinformatics: it is widely used for many classification problems, particularly ones with many features. However, $L_1$ regularization typically selects too many features and that so-called false positives are unavoidable. In this pape…

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

Multinomial Logistic Regression is a well-studied tool for classification and has been widely used in fields like image processing, computer vision and, bioinformatics, to name a few. Under a supervised classification scenario, a Multinomial Logistic Regression model learns a weight vector to differentiate between any …

The paper proves asymptotic normality for multinomial logistic regression on null covariates.

Efficient RL algorithm for multinomial logistic MDPs with provable guarantees.

Multi-task learning has shown to significantly enhance the performance of multiple related learning tasks in a variety of situations. We present the fused logistic regression, a sparse multi-task learning approach for binary classification. Specifically, we introduce sparsity inducing penalties over parameter differenc…

The paper proposes a new method for comparing logistic regression models across different populations.

Revises logistic-softmax likelihood for Bayesian meta-learning in few-shot classification.

Multi-class classification methods based on both labeled and unlabeled functional data sets are discussed. We present a semi-supervised logistic model for classification in the context of functional data analysis. Unknown parameters in our proposed model are estimated by regularization with the help of EM algorithm. A …

Malignant Pleural Mesothelioma (MPM) or malignant mesothelioma (MM) is an atypical, aggressive tumor that matures into cancer in the pleura, a stratum of tissue bordering the lungs. Diagnosis of MPM is difficult and it accounts for about seventy-five percent of all mesothelioma diagnosed yearly in the United States of …

Fast classification for sparse models, even with correlated features.

Adversarial training makes logistic regression weight loss landscapes sharper.

The paper reveals the hidden costs of digitizing commodity money and proposes a new stable-coin system.

Unified framework for sparse logistic regression with nonconvex regularization.

Paper proposes a new method for estimating conditional densities using logistic regressions.

Study explores geometric structure and prior for beta-logistic distribution.

We comment on the fact that gradient ascent for logistic regression has a connection with the perceptron learning algorithm. Logistic learning is the "soft" variant of perceptron learning.

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

Flexible Bayesian approach for generalized linear models, especially for sparse logistic regression.

Annotation errors can significantly hurt classifier performance, yet datasets are only growing noisier with the increased use of Amazon Mechanical Turk and techniques like distant supervision that automatically generate labels. In this paper, we present a robust extension of logistic regression that incorporates the po…

Bayesian logistic regression improves clinical risk prediction models over time.