6 citations · 16 across the 18 of their papers we have counts for
5 papers · 1 filter
Efficient Generative Prediction for EHR Foundation Models: The SCOPE and REACH Estimators
Luke Solo, Matthew B. A. McDermott, William F. Parker +3
Generative foundation models trained on tokenized electronic health record (EHR) timelines show promise for clinical outcome prediction via Monte Carlo sampling of simulated future…
Neuroevolutionary Feature Representations for Causal Inference
Michael C. Burkhart, Gabriel Ruiz
Within the field of causal inference, we consider the problem of estimating heterogeneous treatment effects from data. We propose and validate a novel approach for learning feature…
Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions
Michael C. Burkhart
To minimize the average of a set of log-convex functions, the stochastic Newton method iteratively updates its estimate using subsampled versions of the full objective's gradient a…
A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding
Michael C. Burkhart
Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical f…
The discriminative Kalman filter for nonlinear and non-Gaussian sequential Bayesian filtering
Michael C. Burkhart, David M. Brandman, Carlos E. Vargas-Irwin +1
The Kalman filter (KF) is used in a variety of applications for computing the posterior distribution of latent states in a state space model. The model requires a linear relationsh…