5 papers · 1 filter
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…
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…