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Found 6 papers, 1 papers with code
CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines
Synthetic Electronic Health Records (EHR) have emerged as a pivotal tool in advancing healthcare applications and machine learning models, particularly for researchers without direct access to healthcare data.
Submodularity, pairwise independence and correlation gap
This contrasts with the $e/(e-1)$ bound on the correlation gap ratio for monotone submodular set functions with mutually independent random inputs (which is known to be tight in case (b)), and illustrates a fundamental difference in the behavior of submodular functions with weaker notions of independence.
A Nonparametric Approach with Marginals for Modeling Consumer Choice
The marginal distribution model (MDM) is one such model, that requires only the specification of marginal distributions of the random utilities.
Discrete Optimal Transport with Independent Marginals is #P-Hard
We study the computational complexity of the optimal transport problem that evaluates the Wasserstein distance between the distributions of two K-dimensional discrete random vectors.
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks
CEHR-BERT also demonstrated strong transfer learning capability, as our model trained on only 5% of data outperformed comparison models trained on the entire data set.
Correlation Robust Influence Maximization
We propose a distributionally robust model for the influence maximization problem.
