Paper tackles cybersecurity attack detection with an ensemble approach.
problem Challenges in multi-class classification for cyber security breaches.
method Designing a multi-node multi-class classification ensemble approach.
result Proposed approach outperforms full-data approach in multi-node data-censoring cases.
Paper tackles survival data analysis with positive and unlabeled observations.
problem Traditional survival analysis yields biased results with positive-unlabeled data.
method Developed parametric, nonparametric, and machine learning models for positive and unlabeled survival data.
result Proposed estimation method provides valid results for positive-unlabeled survival data.
Bayesian tree ensemble model for estimating treatment effects in high-dimensional survival data.
problem Estimating heterogeneous treatment effects in censored survival data with many covariates.
method Developed a Bayesian tree ensemble model with a horseshoe prior for adaptive shrinkage.
result Accurately estimates treatment effects in high-dimensional covariate spaces and non-linear functions.
Proposes isotonic regression for calibrating Deep Cox models' survival probabilities.
problem Poor calibration of Deep Cox models' survival probabilities.
method Isotonic regression for post hoc calibration of Deep Cox models.
result Establishes favorable theoretical guarantees and demonstrates empirical effectiveness.
New estimator for survival function with missing not at random censoring indicators.
problem Estimating survival function with missing not at random censoring indicators.
method Proposes a new estimator based on a conditional copula model for the missingness mechanism.
result Provides a new method for estimating conditional survival function with MNAR censoring indicators.
Causal inference analysis is the estimation of the effects of actions on outcomes. In the context of healthcare data this means estimating the outcome of counter-factual treatments (i.e. including treatments that were not observed) on a patient's outcome. Compared to classic machine learning methods, evaluation and val…
WTNN models survival with neural networks for maintenance data.
problem Modeling survival with limited proxy data and censored observations.
method WTNN integrates neural networks with Weibull distribution for time-dependent covariates.
result WTNN produces robust survival predictions improving on existing methods.
Linear regression is arguably the most prominent among statistical inference methods, popular both for its simplicity as well as its broad applicability. On par with data-intensive applications, the sheer size of linear regression problems creates an ever growing demand for quick and cost efficient solvers. Fortunately…
Bayesian Tobit model tackles high-dimensional censored data with Horseshoe prior.
problem High-dimensional censored data with unknown bounds.
method Horseshoe prior for shrinkage, data augmentation for Gibbs sampling.
result Established posterior consistency and concentration rates for Bayesian Tobit models.
Paper proposes kernelized Stein tests for time-to-event data with censoring.
problem Testing goodness-of-fit for time-to-event data with censoring.
method Combining Stein's method and kernelized discrepancies for non-parametric testing.
result Proposed kernelized Stein discrepancy tests perform better than existing methods.
New method for valid prediction intervals with coarsened data.
problem Handling missing data and censored outcomes in training samples.
method Multiply robust conformal risk control with semiparametric theory.
result Stronger coverage properties under covariate shift.