most citedLLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization

2 citations · 2 across the 6 of their papers we have counts for

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stat.ML2026

When Should Humans Step In? Optimal Human Dispatching in AI-Assisted Decisions

Lezhi Tan, Naomi Sagan, Lihua Lei +1

AI systems increasingly assist decision making by producing cheap factor-level assessments of complex inputs, but these assessments can be biased or incomplete. We study selective…

stat.ML2026

Learning When to Trust LLM Priors: A Validated Framework for Semantic Prior Integration

Erica Zhang, Naomi Sagan, Danny Tse +3

Large language models (LLMs) encode rich semantic knowledge that can be useful for supervised learning, but their outputs are unreliable as statistical priors: they may be noisy, m…