
Probabilistic Machine Learning for Healthcare
Machine learning can be used to make sense of healthcare data. Probabili...
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Deep Direct Likelihood Knockoffs
Predictive modeling often uses black box machine learning methods, such ...
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Overfitting and Optimization in Offline Policy Learning
We consider the task of policy learning from an offline dataset generate...
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The Counterfactual χGAN
Causal inference often relies on the counterfactual framework, which req...
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EnergyInspired Models: Learning with SamplerInduced Distributions
Energybased models (EBMs) are powerful probabilistic models, but suffer...
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Population Predictive Checks
Bayesian modeling has become a staple for researchers analyzing data. Th...
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Generalized Control Functions via Variational Decoupling
Causal estimation relies on separating the variation in the outcome due ...
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Reproducibility in Machine Learning for Health
Machine learning algorithms designed to characterize, monitor, and inter...
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Adversarial Examples for Electrocardiograms
Among all physiological signals, electrocardiogram (ECG) has seen some o...
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ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Clinical notes contain information about patients that goes beyond struc...
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Kernelized Complete Conditional Stein Discrepancy
Much of machine learning relies on comparing distributions with discrepa...
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The Random Conditional Distribution for HigherOrder Probabilistic Inference
The need to condition distributional properties such as expectation, var...
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Support and Invertibility in DomainInvariant Representations
Learning domaininvariant representations has become a popular approach ...
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The Variational Predictive Natural Gradient
Variational inference transforms posterior inference into parametric opt...
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Soft Constraints for Inference with Declarative Knowledge
We develop a likelihood free inference procedure for conditioning a prob...
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Opportunities in Machine Learning for Healthcare
Healthcare is a natural arena for the application of machine learning, e...
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Multiple Causal Inference with Latent Confounding
Causal inference from observational data requires assumptions. These ass...
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Noisin: Unbiased Regularization for Recurrent Neural Networks
Recurrent neural networks (RNNs) are powerful models of sequential data....
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Variational Sequential Monte Carlo
Variational inference underlies many recent advances in large scale prob...
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Proximity Variational Inference
Variational inference is a powerful approach for approximate posterior i...
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Hierarchical Implicit Models and LikelihoodFree Variational Inference
Implicit probabilistic models are a flexible class of models defined by ...
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Variational Inference via χUpper Bound Minimization
Variational inference (VI) is widely used as an efficient alternative to...
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Operator Variational Inference
Variational inference is an umbrella term for algorithms which cast Baye...
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Deep Survival Analysis
The electronic health record (EHR) provides an unprecedented opportunity...
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Automatic Differentiation Variational Inference
Probabilistic modeling is iterative. A scientist posits a simple model, ...
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The Variational Gaussian Process
Variational inference is a powerful tool for approximate inference, and ...
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Hierarchical Variational Models
Black box variational inference allows researchers to easily prototype a...
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Dynamic Poisson Factorization
Models for recommender systems use latent factors to explain the prefere...
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Correlated Random Measures
We develop correlated random measures, random measures where the atom we...
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Automatic Variational Inference in Stan
Variational inference is a scalable technique for approximate Bayesian i...
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Deep Exponential Families
We describe deep exponential families (DEFs), a class of latent variable...
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Variational Tempering
Variational inference (VI) combined with data subsampling enables approx...
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Black Box Variational Inference
Variational inference has become a widely used method to approximate pos...
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