Michael C. Burkhart

According to our database, Michael C. Burkhart authored at least 18 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Representation Before Training: A Fixed-Budget Benchmark for Generative Medical Event Models.

Inhyeok Lee, Luke Solo, Michael C. Burkhart, Bashar Ramadan, William F. Parker, Brett K. Beaulieu-Jones

CoRR, April, 2026

Efficient Variance-reduced Estimation from Generative EHR Models: The SCOPE and REACH Estimators.

Luke Solo, Matthew B. A. McDermott, William F. Parker, Bashar Ramadan, Michael C. Burkhart, Brett K. Beaulieu-Jones

CoRR, February, 2026

2025

Prototype Learning to Create Refined Interpretable Digital Phenotypes from ECGs.

Sahil Sethi, David Chen, Michael C. Burkhart, Nipun Bhandari, Bashar Ramadan, Brett K. Beaulieu-Jones

CoRR, August, 2025

Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models.

Michael C. Burkhart, Bashar Ramadan, Luke Solo, William F. Parker, Brett K. Beaulieu-Jones

CoRR, July, 2025

Foundation models for electronic health records: representation dynamics and transferability.

Michael C. Burkhart, Bashar Ramadan, Zewei Liao, Kaveri Chhikara, Juan C. Rojas, William F. Parker, Brett K. Beaulieu-Jones

CoRR, April, 2025

Synthetic data distillation enables the extraction of clinical information at scale.

Elizabeth Geena Woo, Michael C. Burkhart, Emily Alsentzer, Brett K. Beaulieu-Jones

npj Digit. Medicine, 2025

ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning.

Sahil Sethi, David Chen, Thomas Statchen, Michael C. Burkhart, Nipun Bhandari, Bashar Ramadan, Brett K. Beaulieu-Jones

Proceedings of the Machine Learning for Healthcare Conference (MLHC 2025), 2025

2023

Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions.

Michael C. Burkhart

Optim. Lett., April, 2023

Neuroevolutionary representations for learning heterogeneous treatment effects.

Michael C. Burkhart, Gabriel Ruiz

J. Comput. Sci., 2023

2022

Neuroevolutionary Feature Representations for Causal Inference.

Michael C. Burkhart, Gabriel Ruiz

Proceedings of the Computational Science - ICCS 2022, 2022

2021

Discriminative Bayesian Filtering for the Semi-supervised Augmentation of Sequential Observation Data.

Michael C. Burkhart

Proceedings of the Computational Science - ICCS 2021, 2021

2020

The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models.

Michael C. Burkhart, David M. Brandman, Brian Franco, Leigh R. Hochberg, Matthew T. Harrison

Neural Comput., 2020

Deep Low-Density Separation for Semi-supervised Classification.

Michael C. Burkhart, Kyle Shan

Proceedings of the Computational Science - ICCS 2020, 2020

2019

A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding. (Une approche discriminante du filtrage bayésien avec des applications au décodage neuronal humain).

Michael C. Burkhart

PhD thesis, 2019

Adaptive Objective Functions and Distance Metrics for Recommendation Systems.

Michael C. Burkhart, Kourosh Modarresi

Proceedings of the Computational Science - ICCS 2019, 2019

Determining Adaptive Loss Functions and Algorithms for Predictive Models.

Michael C. Burkhart, Kourosh Modarresi

Proceedings of the Computational Science - ICCS 2019, 2019

2018

Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression.

David M. Brandman, Michael C. Burkhart, Jessica N. Kelemen, Brian Franco, Matthew T. Harrison, Leigh R. Hochberg

Neural Comput., 2018

A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding.

Michael C. Burkhart

CoRR, 2018