54 citations · 75 across the 6 of their papers we have counts for
6 papers
Survival Mixture Density Networks
Xintian Han, Mark Goldstein, Rajesh Ranganath
Survival analysis, the art of time-to-event modeling, plays an important role in clinical treatment decisions. Recently, continuous time models built from neural ODEs have been pro…
Inverse-Weighted Survival Games
Xintian Han, Mark Goldstein, Aahlad Puli +3
Deep models trained through maximum likelihood have achieved state-of-the-art results for survival analysis. Despite this training scheme, practitioners evaluate models under other…
X-CAL: Explicit Calibration for Survival Analysis
Mark Goldstein, Xintian Han, Aahlad Puli +2
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…
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach
Carlos Fernández-Loría, Foster Provost, Xintian Han
We examine counterfactual explanations for explaining the decisions made by model-based AI systems. The counterfactual approach we consider defines an explanation as a set of the s…
Adversarial Examples for Electrocardiograms
Xintian Han, Yuxuan Hu, Luca Foschini +3
In recent years, the electrocardiogram (ECG) has seen a large diffusion in both medical and commercial applications, fueled by the rise of single-lead versions. Single-lead ECG can…
A note on quickly sampling a sparse matrix with low rank expectation
Karl Rohe, Jun Tao, Xintian Han +1
Given matrices and with positive elements, this paper proposes an algorithm fastRG to sample a sparse matrix with low rank expec…