
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
While the need for interpretable machine learning has been established, ...
read it

Causal Estimation with Functional Confounders
Causal inference relies on two fundamental assumptions: ignorability and...
read it

XCAL: Explicit Calibration for Survival Analysis
Survival analysis models the distribution of time until an event of inte...
read it

Probabilistic Machine Learning for Healthcare
Machine learning can be used to make sense of healthcare data. Probabili...
read it

Deep Direct Likelihood Knockoffs
Predictive modeling often uses black box machine learning methods, such ...
read it

Overfitting and Optimization in Offline Policy Learning
We consider the task of policy learning from an offline dataset generate...
read it

The Counterfactual χGAN
Causal inference often relies on the counterfactual framework, which req...
read it

EnergyInspired Models: Learning with SamplerInduced Distributions
Energybased models (EBMs) are powerful probabilistic models, but suffer...
read it

Population Predictive Checks
Bayesian modeling has become a staple for researchers analyzing data. Th...
read it

Generalized Control Functions via Variational Decoupling
Causal estimation relies on separating the variation in the outcome due ...
read it

Reproducibility in Machine Learning for Health
Machine learning algorithms designed to characterize, monitor, and inter...
read it

Adversarial Examples for Electrocardiograms
Among all physiological signals, electrocardiogram (ECG) has seen some o...
read it

ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Clinical notes contain information about patients that goes beyond struc...
read it

Kernelized Complete Conditional Stein Discrepancy
Much of machine learning relies on comparing distributions with discrepa...
read it

The Random Conditional Distribution for HigherOrder Probabilistic Inference
The need to condition distributional properties such as expectation, var...
read it

Support and Invertibility in DomainInvariant Representations
Learning domaininvariant representations has become a popular approach ...
read it

The Variational Predictive Natural Gradient
Variational inference transforms posterior inference into parametric opt...
read it

Soft Constraints for Inference with Declarative Knowledge
We develop a likelihood free inference procedure for conditioning a prob...
read it

Opportunities in Machine Learning for Healthcare
Healthcare is a natural arena for the application of machine learning, e...
read it

Multiple Causal Inference with Latent Confounding
Causal inference from observational data requires assumptions. These ass...
read it

Noisin: Unbiased Regularization for Recurrent Neural Networks
Recurrent neural networks (RNNs) are powerful models of sequential data....
read it

Variational Sequential Monte Carlo
Variational inference underlies many recent advances in large scale prob...
read it

Proximity Variational Inference
Variational inference is a powerful approach for approximate posterior i...
read it

Hierarchical Implicit Models and LikelihoodFree Variational Inference
Implicit probabilistic models are a flexible class of models defined by ...
read it

Variational Inference via χUpper Bound Minimization
Variational inference (VI) is widely used as an efficient alternative to...
read it

Operator Variational Inference
Variational inference is an umbrella term for algorithms which cast Baye...
read it

Deep Survival Analysis
The electronic health record (EHR) provides an unprecedented opportunity...
read it

Automatic Differentiation Variational Inference
Probabilistic modeling is iterative. A scientist posits a simple model, ...
read it

The Variational Gaussian Process
Variational inference is a powerful tool for approximate inference, and ...
read it

Hierarchical Variational Models
Black box variational inference allows researchers to easily prototype a...
read it

Dynamic Poisson Factorization
Models for recommender systems use latent factors to explain the prefere...
read it

Correlated Random Measures
We develop correlated random measures, random measures where the atom we...
read it

Automatic Variational Inference in Stan
Variational inference is a scalable technique for approximate Bayesian i...
read it

Deep Exponential Families
We describe deep exponential families (DEFs), a class of latent variable...
read it

Variational Tempering
Variational inference (VI) combined with data subsampling enables approx...
read it

Black Box Variational Inference
Variational inference has become a widely used method to approximate pos...
read it