70 citations · 196 across the 16 of their papers we have counts for
10 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…
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…