arXiv research

Maximum likelihood estimator performance in logistic regression analyzed.

New research shows logistic regression can achieve optimal error rate for agnostic learning of halfspaces.

SMP estimator improves density and logistic regression under misspecification.

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

This study compares logistic regression and XGBoost for predicting credit risk.

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…

GD with large, adaptive stepsizes achieves optimal risk in logistic regression.

Selective neural network improves credit risk prediction while maintaining interpretability.

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

Paper generalizes Gaussian universality and CGMT to dependent data, impacting data augmentation in high-dimensional logistic regression.

Bayesian logistic regression improves clinical risk prediction models over time.

Logistic regression gets a new, simpler uniform bound.

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

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

New algorithm reduces online logistic regression regret without exponential constant.

The study examines how class imbalance impacts logistic regression models in low-default credit portfolios.

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…

We present $α$-loss, $α\in [1,\infty]$, a tunable loss function for binary classification that bridges log-loss ($α=1$) and $0$-$1$ loss ($α= \infty$). We prove that $α$-loss has an equivalent margin-based form and is classification-calibrated, two desirable properties for a good surrogate loss function for the ideal y…

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

Modeling bank leverage dynamics to understand systemic risk in financial markets.

The paper calculates moments and conditional risks for skewed elliptical distributions.

This manuscript studies statistical properties of linear classifiers obtained through minimization of an unregularized convex risk over a finite sample. Although the results are explicitly finite-dimensional, inputs may be passed through feature maps; in this way, in addition to treating the consistency of logistic reg…

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

Gradient descent, when applied to the task of logistic regression, outputs iterates which are biased to follow a unique ray defined by the data. The direction of this ray is the maximum margin predictor of a maximal linearly separable subset of the data; the gradient descent iterates converge to this ray in direction a…

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

In cargo logistics, a key performance measure is transport risk, defined as the deviation of the actual arrival time from the planned arrival time. Neither earliness nor tardiness is desirable for customer and freight forwarders. In this paper, we investigate ways to assess and forecast transport risks using a half-yea…

Hybrid machine learning improves gallstone risk prediction.

Sparse multinomial logistic regression for multiclass classification with feature selection.

Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension management is thus critical. A particular priority is decreasing the incidence of uncontrolled hypertension. Early identification of patients a…

Rotation invariant algorithms fail with hard labels sampled from sparse targets.

Companies try to maximize their profits by recovering returned products of highly uncertain quality and quantity. In this paper, a reverse logistics network for an Original Equipment Manufacturer (OEM) is presented. Returned products are selected for remanufacturing or scrapping, based on their quality and proportional…

Large stepsizes can accelerate gradient descent for logistic regression.

This paper compares ML algorithms for PD prediction, finding XGBoost to be the most effective.

The paper analyzes the maximum margin algorithm's performance on noisy data.

Model predicts higher education dropout risk with interpretable parameters.

Prognostic scores improve logistic regression analysis in RCTs with binary outcomes.

Study calculates tail risk for various mixture distributions.

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

This work proves $L_2$-regularized ERM controls smCE without post-hoc correction.

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…

A new method for distilling predictions from a teacher model to a student model without original training data.

A method for safe online classification reduces test costs while maintaining low error rates.

A new hybrid Newton algorithm improves convergence in logistic regression.

Survival analysis models predict loan write-off risk under IFRS 9.

We consider the problem of link prediction, based on partial observation of a large network, and on side information associated to its vertices. The generative model is formulated as a matrix logistic regression. The performance of the model is analysed in a high-dimensional regime under a structural assumption. The mi…

Efficiently preserves privacy in logistic regression for IoT data.

Large GD stepsizes improve margins and speed up training for non-homogeneous networks.

We consider first order expansions of convex penalized estimators in high-dimensional regression problems with random designs. Our setting includes linear regression and logistic regression as special cases. For a given penalty function $h$ and the corresponding penalized estimator $\hatβ$, we construct a quantity $η$,…