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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…
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…