12 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…
Unique Rashomon Sets for Robust Active Learning
Simon Nguyen, Kentaro Hoffman, Tyler McCormick
Collecting labeled data for machine learning models is often expensive and time-consuming. Active learning addresses this challenge by selectively labeling the most informative obs…
ipd: An R Package for Conducting Inference on Predicted Data
Stephen Salerno, Jiacheng Miao, Awan Afiaz +5
Summary: ipd is an open-source R software package for the downstream modeling of an outcome and its associated features where a potentially sizable portion of the outcome data has…
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…
Some models are useful, but for how long?: A decision theoretic approach to choosing when to refit large-scale prediction models
Kentaro Hoffman, Stephen Salerno, Jeff Leek +1
Large-scale prediction models using tools from artificial intelligence (AI) or machine learning (ML) are increasingly common across a variety of industries and scientific domains.…
From Narratives to Numbers: Valid Inference Using Language Model Predictions from Verbal Autopsy Narratives
Shuxian Fan, Adam Visokay, Kentaro Hoffman +4
In settings where most deaths occur outside the healthcare system, verbal autopsies (VAs) are a common tool to monitor trends in causes of death (COD). VAs are interviews with a su…
Dempster-Shafer P-values: Thoughts on an Alternative Approach for Multinomial Inference
Kentaro Hoffman, Kai Zhang, Tyler McCormick +1
In this paper, we demonstrate that a new measure of evidence we developed called the Dempster-Shafer p-value which allow for insights and interpretations which retain most of the s…
Do We Really Even Need Data?
Kentaro Hoffman, Stephen Salerno, Awan Afiaz +2
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 rat…