13 citations · 18 across the 5 of their papers we have counts for
6 papers · 1 filter
Probabilistic Shapley Value Modeling and Inference
Mert Ketenci, Iñigo Urteaga, Victor Alfonso Rodriguez +2
We propose probabilistic Shapley inference (PSI), a novel probabilistic framework to model and infer sufficient statistics of feature attributions in flexible predictive models, vi…
Maximum Likelihood Estimation of Flexible Survival Densities with Importance Sampling
Mert Ketenci, Shreyas Bhave, Noémie Elhadad +1
Survival analysis is a widely-used technique for analyzing time-to-event data in the presence of censoring. In recent years, numerous survival analysis methods have emerged which s…
Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inference
Mert Ketenci, Adler Perotte, Noémie Elhadad +1
We introduce a novel stochastic variational inference method for Gaussian process () regression, by deriving a posterior over a learnable set of coresets: i.e., over…
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…
Phenotype Inference with Semi-Supervised Mixed Membership Models
Victor Rodriguez, Adler Perotte
Disease phenotyping algorithms process observational clinical data to identify patients with specific diseases. Supervised phenotyping methods require significant quantities of exp…