1.7k citations · 1.7k across the 28 of their papers we have counts for
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Multi-Task Bayesian In-Context Learning
Qingyang Zhu, Eric Karl Oermann, Kyunghyun Cho
Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization. However, exact inference is often intracta…
Large Language Models Predict Functional Outcomes after Acute Ischemic Stroke
Anjali K. Kapoor, Anton Alyakin, Jin Vivian Lee +8
Accurate prediction of functional outcomes after acute ischemic stroke can inform clinical decision-making and resource allocation. Prior work on modified Rankin Scale (mRS) predic…
Gateformer: Advancing Multivariate Time Series Forecasting through Temporal and Variate-Wise Attention with Gated Representations
Yu-Hsiang Lan, Eric K. Oermann
There has been a recent surge of interest in time series modeling using the Transformer architecture. However, forecasting multivariate time series with Transformer presents a uniq…
Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactions
Sully F. Chen, Robert J. Steele, Glen M. Hocky +3
The transformer architecture has revolutionized bioinformatics and driven progress in the understanding and prediction of the properties of biomolecules. To date, most biosequence…
Identifying and mitigating bias in algorithms used to manage patients in a pandemic
Yifan Li, Garrett Yoon, Mustafa Nasir-Moin +4
Numerous COVID-19 clinical decision support systems have been developed. However many of these systems do not have the merit for validity due to methodological shortcomings includi…
Patient level simulation and reinforcement learning to discover novel strategies for treating ovarian cancer
Brian Murphy, Mustafa Nasir-Moin, Grace von Oiste +4
The prognosis for patients with epithelial ovarian cancer remains dismal despite improvements in survival for other cancers. Treatment involves multiple lines of chemotherapy and b…
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations
Aahlad Puli, Lily H. Zhang, Eric K. Oermann +1
In many prediction problems, spurious correlations are induced by a changing relationship between the label and a nuisance variable that is also correlated with the covariates. For…