9 papers
Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery
Tyler H. McCormick
Modern Machine Learning (ML) and Artificial Intelligence (AI) models, especially large language models (LLMs), are increasingly used to generate scientific hypotheses and mechanist…
Rashomon-Seeded Annealing for Robust Bayesian Inference in Factorial Designs
Yiyang Fan, Soumyakanti Pan, Tyler H. McCormick
Integrating over model uncertainty in factorial designs via Bayesian model averaging is hindered by the combinatorial explosion of interpretable interaction effects, often yielding…
REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees
Simon D. Nguyen, Hayden McTavish, Kentaro Hoffman +2
Active learning reduces labeling costs by selecting samples that maximize information gain. A dominant framework, Query-by-Committee (QBC), typically relies on perturbation-based d…
Adaptive Active Learning for Regression via Reinforcement Learning
Simon D. Nguyen, Troy Russo, Kentaro Hoffman +1
Active learning for regression reduces labeling costs by selecting the most informative samples. Improved Greedy Sampling is a prominent method that balances feature-space diversit…
Do We Really Even Need Data? A Modern Look at Drawing Inference with Predicted Data
Stephen Salerno, Kentaro Hoffman, Awan Afiaz +3
As artificial intelligence and machine learning tools become more accessible, and scientists face new obstacles to data collection (e.g., rising costs, declining survey response ra…
What's the Weight? Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research
Stephen Salerno, Emily K. Roberts, Belinda L. Needham +4
In this work, we are motivated by the problem of estimating racial disparities in health outcomes, specifically the average controlled difference (ACD) in telomere length between B…