156 citations · 214 across the 13 of their papers we have counts for
4 papers · 1 filter
Learning Invariant Representations with Missing Data
Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau +4
Spurious correlations allow flexible models to predict well during training but poorly on related test distributions. Recent work has shown that models that satisfy particular inde…
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…
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
Lily H. Zhang, Mark Goldstein, Rajesh Ranganath
Deep generative models (DGMs) seem a natural fit for detecting out-of-distribution (OOD) inputs, but such models have been shown to assign higher probabilities or densities to OOD…
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…