Traditional statistical and machine learning methods assume identical distribution for the
training and test data sets. This assumption, however, is often violated in real applications …

Survival analysis is an important problem in healthcare because it models the relationship
between an individual's covariates and the onset time of an event of interest (eg, death). It is …

Survival models derived from health care data are an important support to inform critical
screening and therapeutic decisions. Most models however, do not generalize to …

Reliable and accurate risk prediction is fundamental for successful management of clinical
conditions. Estimating comprehensive risk prediction models precisely, however, is a difficult …

Survival analysis models the distribution of time until an event of interest, such as discharge
from the hospital or admission to the ICU. When a model's predicted number of events within …

We propose a general approach for training survival analysis models that minimizes a worst-
case error across all subpopulations that are large enough (occurring with at least a user …

We study the problem of inferring heterogeneous treatment effects from time-to-event data.
While both the related problems of (i) estimating treatment effects for binary or continuous …

When data are right-censored, ie some outcomes are missing due to a limited period of
observation, survival analysis can compute the" time to event". Multiple classes of outcomes …

The conditional survival function of a time-to-event outcome subject to censoring and
truncation is a common target of estimation in survival analysis. This parameter may be of …

The accelerated failure time (AFT) model and Cox proportional hazards (PH) model are
broadly used for survival endpoints of primary interest. However, the estimation efficiency …