1 code implementation • 23 Aug 2022 • Xintian Han, Mark Goldstein, Rajesh Ranganath
Survival MDN applies an invertible positive function to the output of Mixture Density Networks (MDNs).
1 code implementation • NeurIPS 2021 • Xintian Han, Mark Goldstein, Aahlad Puli, Thomas Wies, Adler J Perotte, Rajesh Ranganath
When the loss is proper, we show that the games always have the true failure and censoring distributions as a stationary point.
1 code implementation • NeurIPS 2020 • Mark Goldstein, Xintian Han, Aahlad Puli, Adler J. Perotte, Rajesh Ranganath
A survival model's calibration can be measured using, for instance, distributional calibration (D-CALIBRATION) [Haider et al., 2020] which computes the squared difference between the observed and predicted number of events within different time intervals.
no code implementations • 21 Jan 2020 • Carlos Fernández-Loría, Foster Provost, Xintian Han
We examine counterfactual explanations for explaining the decisions made by model-based AI systems.
no code implementations • 13 May 2019 • Xintian Han, Yuxuan Hu, Luca Foschini, Larry Chinitz, Lior Jankelson, Rajesh Ranganath
For this model, we utilized a new technique to generate smoothed examples to produce signals that are 1) indistinguishable to cardiologists from the original examples and 2) incorrectly classified by the neural network.
no code implementations • 9 Apr 2019 • Raghav Singhal, Xintian Han, Saad Lahlou, Rajesh Ranganath
We introduce kernelized complete conditional Stein discrepancies (KCC-SDs).