A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding. - Michael C. Burkhart (CoRR, 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)

Determining Adaptive Loss Functions and Algorithms for Predictive Models. - Michael C. Burkhart; Kourosh Modarresi (ICCS (2), 2019)

Adaptive Objective Functions and Distance Metrics for Recommendation Systems. - Michael C. Burkhart; Kourosh Modarresi (ICCS (2), 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 (2019)

Deep Low-Density Separation for Semi-supervised Classification. - Michael C. Burkhart; Kyle Shan (ICCS (3), 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)

Discriminative Bayesian Filtering for the Semi-supervised Augmentation of Sequential Observation Data. - Michael C. Burkhart (ICCS (2), 2021)

Neuroevolutionary Feature Representations for Causal Inference. - Michael C. Burkhart; Gabriel Ruiz (ICCS (1), 2022)

Neuroevolutionary representations for learning heterogeneous treatment effects. - Michael C. Burkhart; Gabriel Ruiz (J. Comput. Sci., 2023)

Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions. - Michael C. Burkhart (Optim. Lett., 2023)

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 (CoRR, 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, 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, 2025)