Logistic Regression
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Systematic estimates of global causes of neonatal and under 5 mortality in 2000-24: secondary data analysis using bayesian multinomial logistic regression
AbstractObjectiveTo estimate cause specific mortality among neonates and children under 5 for 195 countries from 2000 to 2024.DesignSecondary data analysis using a Bayesian multinomial logistic regression model to estimate cause specific mortality fractions. Data sourcesPubMed, Embase, Web of Science, SCOPUS, Cochrane, Global Health Index Medicus, PAHO, Global Health OVID, Africa-Wide Information, IndMed, WHO Mortality Database, Demographic and Health Surveys (DHS), Multiple Indicator...
Estimates of global causes of death for children and adolescents aged 5-19 in 2000-24: secondary data analysis using bayesian multinomial logistic regression
AbstractObjectiveTo estimate cause specific mortality among children and adolescents aged 5-19 years for 195 countries from 2000 to 2024.DesignSecondary data analysis using a bayesian multinomial logistic regression model to estimate cause specific mortality fractions. Data sourcesPubMed, Embase, Web of Science, Scopus, Cochrane Library, Global Index Medicus, Pan American Health Organization, Global Health Ovid, Africa-Wide Information, IndMed, WHO Mortality Database, Demographic and Health...
Hard labels sampled from sparse targets mislead rotation invariant algorithms
Announce Type: replace-cross Abstract: One of the most common machine learning setups is logistic regression. In many classification models, including neural networks, the final prediction is obtained by applying a logistic link function to a linear score. In binary logistic regression, the feedback can be either soft labels, corresponding to the true conditional probability of the data (as in distillation), or sampled hard labels (taking values $\pm 1$).
Convergence of Steepest Descent and Adam under Non-Uniform Smoothness
arXiv:2605.30648v1 Announce Type: new Abstract: Recent work has analyzed the convergence of first-order methods under non-uniform smoothness assumptions that better model the loss landscape in machine learning tasks. We generalize this assumption to objectives whose curvature is an affine function of the objective value. This property is satisfied by a broad class of problems, including logistic regression, generalized linear models with a logistic link function, softmax policy gradient in...
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
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TinyML-Driven Cybersecurity for Autonomous Spacecraft: Latency-Accuracy Analysis for SPARTA RF and Cyber Threat Detection
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Analysis of Ethnic Disparities in Autism Spectrum Disorder among Toddlers
arXiv:2606.01217v1 Announce Type: new Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by challenges in communication and behavior. This study examines the relationship between ethnicity and ASD traits, along with behavioural scores, sex and neonatal jaundice across three ethnic groups: White Europeans, Asians, and Middle Eastern individuals. We perform a logistic regression and show that ethnicity has a significant effect on incidence of ASD.
From Genes to Tokens: a GWAS-inspired Approach for Interpretable Stylometric Analysis
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Disentangling conviction and conformity: a Bayesian ideal point model of voting behaviour in online debates
arXiv:2606.03786v1 Announce Type: new Abstract: Online debate platforms offer a unique window into the mechanisms driving opinion formation: they capture both explicit political preferences and the peer environment in which those preferences are expressed. In this work, I develop a Bayesian logistic regression model, inspired by ideal point models from political science, to disentangle two competing mechanisms of voting behaviour in online debates: conviction, driven by prior ideological...
Self-Certifying Transport MCMC via Dual Spectral-Gap Certificates
arXiv:2605.30722v1 Announce Type: new Abstract: We propose CerT-MCMC, a framework that equips learned-transport Markov chain Monte Carlo with automatic, rigorous convergence certificates. A normalising flow maps a Gaussian reference to an approximation of the target posterior; the same flow then serves as both the independence Metropolis-Hastings proposal and the basis for a computable spectral-gap bound. We develop two complementary certificates.