Michael C. Burkhart

21 papers Journal 14Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Efficient Variance-reduced Estimation from Generative EHR Models: The SCOPE and REACH Estimators. CoRR Luke Solo, Matthew B. A. McDermott, William F. Parker, Bashar Ramadan, Michael C. Burkhart, Brett K. Beaulieu-Jones
2025	J	`jnl`	Foundation models for electronic health records: representation dynamics and transferability. CoRR Michael C. Burkhart, Bashar Ramadan, Zewei Liao, Kaveri Chhikara, Juan C. Rojas, William F. Parker, Brett K. Beaulieu-Jones
2025	—	`conf`	ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning. MLHC Sahil Sethi, David Chen, Thomas Statchen, Michael C. Burkhart, Nipun Bhandari, Bashar Ramadan, Brett K. Beaulieu-Jones
2025	J	`jnl`	ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning. CoRR Sahil Sethi, David Chen, Thomas Statchen, Michael C. Burkhart, Nipun Bhandari, Bashar Ramadan, Brett K. Beaulieu-Jones
2025	J	`jnl`	Prototype Learning to Create Refined Interpretable Digital Phenotypes from ECGs. CoRR Sahil Sethi, David Chen, Michael C. Burkhart, Nipun Bhandari, Bashar Ramadan, Brett K. Beaulieu-Jones
2025	J	`jnl`	Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models. CoRR Michael C. Burkhart, Bashar Ramadan, Luke Solo, William F. Parker, Brett K. Beaulieu-Jones
2025	J	`jnl`	Synthetic data distillation enables the extraction of clinical information at scale. npj Digit. Medicine Elizabeth Geena Woo, Michael C. Burkhart, Emily Alsentzer, Brett K. Beaulieu-Jones
2023	J	`jnl`	Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions. Optim. Lett. Michael C. Burkhart
2023	J	`jnl`	Neuroevolutionary representations for learning heterogeneous treatment effects. J. Comput. Sci. Michael C. Burkhart, Gabriel Ruiz
2022	J	`jnl`	Deep Low-Density Separation for Semi-Supervised Classification. CoRR Michael C. Burkhart, Kyle Shan
2022	—	`conf`	Neuroevolutionary Feature Representations for Causal Inference. ICCS (1) Michael C. Burkhart, Gabriel Ruiz
2022	J	`jnl`	Neuroevolutionary Feature Representations for Causal Inference. CoRR Michael C. Burkhart, Gabriel Ruiz
2021	J	`jnl`	Discriminative Bayesian Filtering Lends Momentum to the Stochastic Newton Method for Minimizing Log-Convex Functions. CoRR Michael C. Burkhart
2021	—	`conf`	Discriminative Bayesian Filtering for the Semi-supervised Augmentation of Sequential Observation Data. ICCS (2) Michael C. Burkhart
2020	—	`conf`	Deep Low-Density Separation for Semi-supervised Classification. ICCS (3) Michael C. Burkhart, Kyle Shan
2020	J	`jnl`	The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models. Neural Comput. Michael C. Burkhart, David M. Brandman, Brian Franco, Leigh R. Hochberg, Matthew T. Harrison
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	—	`conf`	Adaptive Objective Functions and Distance Metrics for Recommendation Systems. ICCS (2) Michael C. Burkhart, Kourosh Modarresi
2019	—	`conf`	Determining Adaptive Loss Functions and Algorithms for Predictive Models. ICCS (2) Michael C. Burkhart, Kourosh Modarresi
2018	J	`jnl`	A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding. CoRR Michael C. Burkhart
2018	J	`jnl`	Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression. Neural Comput. David M. Brandman, Michael C. Burkhart, Jessica N. Kelemen, Brian Franco, Matthew T. Harrison, Leigh R. Hochberg