arXiv research

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

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…

New method tackles parcel routing with AI.

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

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

Paper finds dropout noise approximation invalid for logistic regression and neural networks.

Flat solutions don't guarantee generalization for logistic loss in neural networks.

The paper examines logistic regression in sparse network settings, improving inference under varying degrees of dyadic dependence.

This paper compares neural networks with traditional methods for churn prediction using financial data.

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

A combined model integrates latent factor and logistic regression for citation network analysis.

A new method for Bayesian neural networks using probabilistic backpropagation.

Logistic LDA applies LDA to non-categorical data.

New algorithms reduce regret in neural logistic bandits.

The paper proves neural networks' consistency and optimal convergence rates for various function classes.

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

This paper uses robust optimization to analyze supply chain resilience.

Early stopping improves neural networks' performance on binary classification tasks.

Novel oracle-type inequality for logistic loss in DNNs achieves sharp convergence rates.

Training feedforward neural networks with standard logistic activations is considered difficult because of the intrinsic properties of these sigmoidal functions. This work aims at showing that these networks can be trained to achieve generalization performance comparable to those based on hyperbolic tangent activations…

The pseudo-likelihood method is one of the most popular algorithms for learning sparse binary pairwise Markov networks. In this paper, we formulate the $L_1$ regularized pseudo-likelihood problem as a sparse multiple logistic regression problem. In this way, many insights and optimization procedures for sparse logistic…

We apply the network Lasso to classify partially labeled data points which are characterized by high-dimensional feature vectors. In order to learn an accurate classifier from limited amounts of labeled data, we borrow statistical strength, via an intrinsic network structure, across the dataset. The resulting logistic …

This paper provides an overview of activation functions in neural networks.

Selective neural network improves credit risk prediction while maintaining interpretability.

The paper models reverse logistics network design considering product uncertainty and risk.

Paper derives convergence rates for NPMLE in Hellinger distance using deep neural networks.

We show how to use a variational approximation to the logistic function to perform approximate inference in Bayesian networks containing discrete nodes with continuous parents. Essentially, we convert the logistic function to a Gaussian, which facilitates exact inference, and then iteratively adjust the variational par…

Estimates non-parametric logistic model using case-control data and external summary info.

Gradient descent in neural networks maximizes margin.

Analysis of gradient descent on wide neural networks reveals strong generalization.

We revisit logistic regression and its nonlinear extensions, including multilayer feedforward neural networks, by showing that these classifiers can be viewed as converting input or higher-level features into Dempster-Shafer mass functions and aggregating them by Dempster's rule of combination. The probabilistic output…

Enhances neural networks in hyperbolic space for better data structure capture.

Research in statistical relational learning has produced a number of methods for learning relational models from large-scale network data. While these methods have been successfully applied in various domains, they have been developed under the unrealistic assumption of full data access. In practice, however, the data …

Logistic regression with wavelets achieves bacterial infection detection accuracy.

Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in comparison to the traditional method of Logistic Regression. In addition, it has be…

Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for …

New mechanism protects neural network weights from privacy attacks during self-supervised learning.

This paper models how features influence event triggers in high-dimensional networks.

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…

Logistic regression for brain imaging without p-values.

MPNN improves on UniFL approximation with provable guarantees.

Deep learning tool classifies urban delivery vehicles.

Improved few-shot learning with interpretable models.

Deep neural networks classify chaotic time series.

Study analyzes factors influencing healthcare providers' engagement with SMS campaigns.

Anomaly detection model for large networks identifies attacks with reduced false positives.

This paper revisits the special type of a neural network known under two names. In the statistics and machine learning community it is known as a multi-class logistic regression neural network. In the neural network community, it is simply the soft-max layer. The importance is underscored by its role in deep learning: …

01 loss is robust to outliers and adversarial attacks, outperforming convex losses.