70 citations · 191 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
The Counterfactual -GAN
Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath +1
Causal inference often relies on the counterfactual framework, which requires that treatment assignment is independent of the outcome, known as strong ignorability. Approaches to e…
Energy-Inspired Models: Learning with Sampler-Induced Distributions
Dieterich Lawson, George Tucker, Bo Dai +1
Energy-based models (EBMs) are powerful probabilistic models, but suffer from intractable sampling and density evaluation due to the partition function. As a result, inference in E…
Reproducibility in Machine Learning for Health
Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek +3
Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially…
General Control Functions for Causal Effect Estimation from Instrumental Variables
Aahlad Manas Puli, Rajesh Ranganath
Causal effect estimation relies on separating the variation in the outcome into parts due to the treatment and due to the confounders. To achieve this separation, practitioners oft…