arXiv research

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

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

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.

Fast classification for sparse models, even with correlated features.

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 algorithm reduces regret for logistic bandits without $κ$ dependency.

Develops first optimal algorithm for logistic bandits.

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

This paper concerns a method of selecting a subset of features for a sequential logit model. Tanaka and Nakagawa (2014) proposed a mixed integer quadratic optimization formulation for solving the problem based on a quadratic approximation of the logistic loss function. However, since there is a significant gap between …

Improved sketching for logistic and $\ell_1$ regression with near-linear dimensions.

Paper introduces data-dependent SSP for private linear and logistic regression.

Novel bounds for logistic regression coreset construction and feature selection.

The article describe the model, derivation, and implementation of variational Bayesian inference for linear and logistic regression, both with and without automatic relevance determination. It has the dual function of acting as a tutorial for the derivation of variational Bayesian inference for simple models, as well a…

PbP strategy improves logistic model prediction with missing values.

Improved regret bound for multinomial logistic bandits with non-linearity.

Adversarial training makes logistic regression weight loss landscapes sharper.

Enhances KLR for indefinite kernels with $L_1$-norm regularization.

Study compares forecasting methods for logistics time series.

A successful approach to structured learning is to write the learning objective as a joint function of linear parameters and inference messages, and iterate between updates to each. This paper observes that if the inference problem is "smoothed" through the addition of entropy terms, for fixed messages, the learning ob…

New method for GLMs under DP provides private uncertainty quantification.

Improved estimator reduces bias in statistical learning models.

New algorithm learns sparse GLMs for binary outcomes efficiently.

Transformers can approximate Newton's method for logistic regression.

SMP estimator improves density and logistic regression under misspecification.

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…

The standard linear and logistic regression models assume that the response variables are independent, but share the same linear relationship to their corresponding vectors of covariates. The assumption that the response variables are independent is, however, too strong. In many applications, these responses are collec…

Improved algorithm for logistic bandits with better regret bounds.

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

We consider support recovery in the quadratic logistic regression setting - where the target depends on both p linear terms $x_i$ and up to $p^2$ quadratic terms $x_i x_j$. Quadratic terms enable prediction/modeling of higher-order effects between features and the target, but when incorporated naively may involve solvi…

Cyanure offers efficient solvers for linear model learning in Python, C++, and more.

A new algorithm approximates logistic regression probabilities efficiently.

We consider high-dimensional binary classification by sparse logistic regression. We propose a model/feature selection procedure based on penalized maximum likelihood with a complexity penalty on the model size and derive the non-asymptotic bounds for the resulting misclassification excess risk. The bounds can be reduc…

Selective neural network improves credit risk prediction while maintaining interpretability.

The paper revisits discriminative vs. generative classifiers, showing naive Bayes requires fewer samples.

New algorithm recovers sparse binary vectors from generalized linear measurements efficiently.

Efficient RL algorithm for multinomial logistic MDPs with provable guarantees.

New algorithm solves $\ell_0$-norm constrained multilinear logistic regression for tensor data.

For the problem of multi-class linear classification and feature selection, we propose approximate message passing approaches to sparse multinomial logistic regression (MLR). First, we propose two algorithms based on the Hybrid Generalized Approximate Message Passing (HyGAMP) framework: one finds the maximum a posterio…

A new method for Bayesian neural networks using probabilistic backpropagation.

Introduces Soft-SVM for binary classification bridging logistic and SVM.

2 Diabetes is a leading worldwide public health concern, and its increasing prevalence has significant health and economic importance in all nations. The condition is a multifactorial disorder with a complex aetiology. The genetic determinants remain largely elusive, with only a handful of identified candidate genes. G…

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

Adam optimizes linear classifiers with separable data.

Grokking occurs in simple binary logistic classification near linear separability and noise.

New method for efficiently deleting data from ML models.

Multivariate binary data is becoming abundant in current biological research. Logistic principal component analysis (PCA) is one of the commonly used tools to explore the relationships inside a multivariate binary data set by exploiting the underlying low rank structure. We re-expressed the logistic PCA model based on …

Deep learning model explains breast cancer subtypes using logistic regression.