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Found 15 papers, 6 papers with code
Large-scale Training of Foundation Models for Wearable Biosignals
Tracking biosignals is crucial for monitoring wellness and preempting the development of severe medical conditions.
Label Shift Estimators for Non-Ignorable Missing Data
We consider the problem of estimating the mean of a random variable Y subject to non-ignorable missingness, i. e., where the missingness mechanism depends on Y .
Simulation-based Inference for Cardiovascular Models
Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico.
Learning Invariant Representations with Missing Data
Enforcing such independencies requires nuisances to be observed during training.
Breiman's two cultures: You don't have to choose sides
And while these categories represent extreme points in model space, modern computational and algorithmic tools enable us to interpolate between these points, producing flexible, interpretable, and scientifically-informed hybrids that can enjoy accurate and robust predictions, and resolve issues with data analysis that Breiman describes, such as the Rashomon effect and Occam's dilemma.
Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance
We propose using an evaluation model $-$ a model that describes the conditional distribution of the predictive model score $-$ to form model-based metric (MBM) estimates.
Representing and Denoising Wearable ECG Recordings
In this work, we develop a statistical model to simulate a structured noise process in ECGs derived from a wearable sensor, design a beat-to-beat representation that is conducive for analyzing variation, and devise a factor analysis-based method to denoise the ECG.
Learning Insulin-Glucose Dynamics in the Wild
We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics.
A Probabilistic Model of Cardiac Physiology and Electrocardiograms
An electrocardiogram (EKG) is a common, non-invasive test that measures the electrical activity of a patient's heart.
Measuring the Stability of EHR- and EKG-based Predictive Models
In a predictive task, we show that EKG-based models can be more stable than EHR-based models across different patient populations.
Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets.
Semi-Amortized Variational Autoencoders
Amortized variational inference (AVI) replaces instance-specific local inference with a global inference network.
Ranked #2 on
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Reducing Reparameterization Gradient Variance
Optimization with noisy gradients has become ubiquitous in statistics and machine learning.
Variational Boosting: Iteratively Refining Posterior Approximations
We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class.
Recurrent switching linear dynamical systems
Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics.
