312 citations · 649 across the 29 of their papers we have counts for
16 papers · 1 filter
Copula-Based Deep Survival Models for Dependent Censoring
Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner +1
A survival dataset describes a set of instances (e.g. patients) and provides, for each, either the time until an event (e.g. death), or the censoring time (e.g. when lost to follow…
An Effective Meaningful Way to Evaluate Survival Models
Shi-ang Qi, Neeraj Kumar, Mahtab Farrokh +5
One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by…
Modeling and Forecasting COVID-19 Cases using Latent Subpopulations
Roberto Vega, Zehra Shah, Pouria Ramazi +1
Classical epidemiological models assume homogeneous populations. There have been important extensions to model heterogeneous populations, when the identity of the sub-populations i…
Domain-shift adaptation via linear transformations
Roberto Vega, Russell Greiner
A predictor, , learned with data from a source domain (A) might not be accurate on a target domain (B) when their distributions are different. Domain adaptation aims…
Variational Auto-Encoder Architectures that Excel at Causal Inference
Negar Hassanpour, Russell Greiner
Estimating causal effects from observational data (at either an individual -- or a population -- level) is critical for making many types of decisions. One approach to address this…
SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting
Roberto Vega, Leonardo Flores, Russell Greiner
Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR…