arXiv research

Maximum likelihood estimator performance in logistic regression analyzed.

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

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

Novel bounds for logistic regression coreset construction and feature selection.

Unified framework for sparse logistic regression with nonconvex regularization.

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.

Paper explains learning property of logistic and softmax losses for balanced and imbalanced class data.

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

Safe screening rules reduce computation time in logistic regression with $\ell_0-\ell_2$ regularization.

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…

Paper introduces imprecise logistic regression for handling uncertain data.

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

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

A new data-oblivious sketch for logistic regression reduces data size while maintaining approximation accuracy.

The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection. Although many recent efforts have been devoted to its efficient implementation, its application to high dimensional data still poses significant challenges. In this paper…

PIANO speeds up multinomial logistic regression solving.

City Logistics is characterized by multiple stakeholders that often have different views of such a complex system. From a public policy perspective, identifying stakeholders, issues and trends is a daunting challenge, only partially addressed by traditional observation systems. Nowadays, social media is one of the bigg…

Logistic regression is a widely used method in several fields. When applying logistic regression to imbalanced data, for which majority classes dominate over minority classes, all class labels are estimated as `majority class.' In this article, we use an F-measure optimization method to improve the performance of logis…

Develops first optimal algorithm for logistic bandits.

A new algorithm approximates logistic regression probabilities efficiently.

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

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

Study shows sample complexity for logistic regression with normal covariates.

Logistic regression gets a new, simpler uniform bound.

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

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

SGD converges globally to logistic loss minima for two-layer nets.

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

Gradient descent with logistic loss can make two-layer networks interpolate binary classification data.

Improves logistic regression performance with nonconvex programming.

Study compares forecasting methods for logistics time series.

Solving logistic regression with L1-regularization in distributed settings is an important problem. This problem arises when training dataset is very large and cannot fit the memory of a single machine. We present d-GLMNET, a new algorithm solving logistic regression with L1-regularization in the distributed settings. …

Coresets are one of the central methods to facilitate the analysis of large data sets. We continue a recent line of research applying the theory of coresets to logistic regression. First, we show a negative result, namely, that no strongly sublinear sized coresets exist for logistic regression. To deal with intractable…

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

Improved classifier for PU data using logistic regression.

Proposes MELODIC family for simultaneous binary logistic regression.

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…

GD at EoS edge minimizes logistic loss without monotonic convergence.

New method tackles parcel routing with AI.

Early stopping improves logistic regression's calibration and consistency in high dimensions.

This work improves regret minimization for logistic bandits by reducing dependence on a large constant.

A neural network solves logistic regression with $\ell_1$ regularization efficiently.

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…

Gradient descent with logistic loss can interpolate deep networks with smoothed ReLU activations under certain conditions.

We apply the network Lasso to solve binary classification and clustering problems for network-structured data. To this end, we generalize ordinary logistic regression to non-Euclidean data with an intrinsic network structure. The resulting "logistic network Lasso" amounts to solving a non-smooth convex regularized empi…

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…