222 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of MichiganUS69 papers
- Cornell UniversityUS7 papers
- Harvard UniversityUS5 papers
- Northwestern UniversityUS5 papers
- Argonne National LaboratoryUS4 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- Northeastern UniversityUS4 papers
- The University of Texas at AustinUS4 papers
- University of ArizonaUS4 papers
- University of MinnesotaUS4 papers
- Ann Arbor Center for Independent LivingUS3 papers
- Boston UniversityUS3 papers
5 papers · 1 filter
Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
Donna Tjandra, Trenton Chang, Sonali Parbhoo +8
Objective: The growing availability of large-scale observational clinical datasets and challenges in conducting randomized controlled trials have spurred enthusiasm in using causal…
An Overview of Low-Rank Structures in the Training and Adaptation of Large Models
Laura Balzano, Tianjiao Ding, Benjamin D. Haeffele +5
The substantial computational demands of modern large-scale deep learning present significant challenges for efficient training and deployment. Recent research has revealed a wides…
From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions
Trenton Chang, Jenna Wiens
Selective labels occur when label observations are subject to a decision-making process; e.g., diagnoses that depend on the administration of laboratory tests. We study a clinicall…
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure
Heming Yao, Harm Derksen, Jessica R. Golbus +4
A model's interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is pr…
Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale
Eli Sherman, Hitinder Gurm, Ulysses Balis +2
In healthcare, patient risk stratification models are often learned using time-series data extracted from electronic health records. When extracting data for a clinical prediction…