1 citations · 1 across the 3 of their papers we have counts for
6 papers
Fixed-Size Active Statistical Inference
Erik Skalnes, Michael Oberst
Variance reduction techniques improve the efficiency of drawing conclusions from data. Recent proposals for variance-reduction use two complementary techniques to reduce variance:…
Evaluating Rubric Generation with Interventional Transfer
Erik Skalnes, Layne C. Price, Raviteja Anantha +1
Instance-specific rubrics are common in AI benchmarks where reliable evaluation requires specific expert knowledge. This approach is difficult to scale, prompting research into the…
Verifiable by Construction: Claim-Level Evaluation of Verbatim Citation in Clinical Question Answering
Jiashuo Zhang, Yuling Chen, Yvonne Commodore-Mensah +1
Large language models (LLMs) have been widely adopted for clinical question answering (QA). Current systems can attach citations to their answers, but these often point to broad te…
Bounding the Causal Impact of ML-assisted Decision-Making via Counterfactual Correctness
Jonathan Zhang, Erik Skalnes, Jacob Chen +1
Predictive machine learning (ML) models are increasingly used to aid human decision-makers across various high-risk domains such as healthcare and criminal justice. There is a grow…
Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context
Andrew Wang, Jiashuo Zhang, Michael Oberst
Public datasets of Chest X-Rays (CXRs) have long been a popular benchmark for developing machine learning (ML) computer vision models in healthcare. However, the reported strong av…
Towards Virtual Clinical Trials of Radiology AI with Conditional Generative Modeling
Benjamin D. Killeen, Bohua Wan, Aditya V. Kulkarni +4
Artificial intelligence (AI) is poised to transform healthcare by enabling personalized and efficient care through data-driven insights. Although radiology is at the forefront of A…