4 papers
Conformal Prediction for Regression with Clipped Outcomes
Matteo Sesia, Vladimir Svetnik
We study conformal prediction for regression using calibration data with outcomes that are doubly censored (clipped) at known fixed thresholds. We show that existing methods are un…
Distribution-Free Selection of Low-Risk Oncology Patients for Survival Beyond a Time Horizon
Matteo Sesia, Vladimir Svetnik
We study the problem of selecting a subset of patients who are unlikely to experience an adverse event within a fixed time horizon by calibrating a screening rule based on a black-…
Conformal Survival Bands for Risk Screening under Right-Censoring
Matteo Sesia, Vladimir Svetnik
We propose a method to quantify uncertainty around individual survival distribution estimates using right-censored data, compatible with any survival model. Unlike classical confid…
Doubly Robust Conformalized Survival Analysis with Right-Censored Data
Matteo Sesia, Vladimir Svetnik
We present a conformal inference method for constructing lower prediction bounds for survival times from right-censored data, extending recent approaches designed for more restrict…