618 citations · 1.9k across the 59 of their papers we have counts for
11 papers · 1 filter
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…
Holdout predictive checks for Bayesian model criticism
Gemma E. Moran, David M. Blei, Rajesh Ranganath
Bayesian modeling helps applied researchers articulate assumptions about their data and develop models tailored for specific applications. Thanks to good methods for approximate po…
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…
Adversarial Examples for Electrocardiograms
Xintian Han, Yuxuan Hu, Luca Foschini +3
In recent years, the electrocardiogram (ECG) has seen a large diffusion in both medical and commercial applications, fueled by the rise of single-lead versions. Single-lead ECG can…
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Kexin Huang, Jaan Altosaar, Rajesh Ranganath
Clinical notes contain information about patients that goes beyond structured data like lab values and medications. However, clinical notes have been underused relative to structur…
Kernelized Complete Conditional Stein Discrepancy
Raghav Singhal, Xintian Han, Saad Lahlou +1
Much of machine learning relies on comparing distributions with discrepancy measures. Stein's method creates discrepancy measures between two distributions that require only the un…
The Random Conditional Distribution for Higher-Order Probabilistic Inference
Zenna Tavares, Xin Zhang, Edgar Minaysan +3
The need to condition distributional properties such as expectation, variance, and entropy arises in algorithmic fairness, model simplification, robustness and many other areas. At…
Support and Invertibility in Domain-Invariant Representations
Fredrik D. Johansson, David Sontag, Rajesh Ranganath
Learning domain-invariant representations has become a popular approach to unsupervised domain adaptation and is often justified by invoking a particular suite of theoretical resul…
The Variational Predictive Natural Gradient
Da Tang, Rajesh Ranganath
Variational inference transforms posterior inference into parametric optimization thereby enabling the use of latent variable models where otherwise impractical. However, variation…
Soft Constraints for Inference with Declarative Knowledge
Zenna Tavares, Javier Burroni, Edgar Minaysan +2
We develop a likelihood free inference procedure for conditioning a probabilistic model on a predicate. A predicate is a Boolean valued function which expresses a yes/no question a…