3 papers
cs.LG2026
KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching
Siddhartha Laghuvarapu, Rohan Deb, Jimeng Sun
Uncertainty quantification is essential for deploying machine learning models in high-stakes domains such as scientific discovery and healthcare. Conformal Prediction (CP) provides…
cs.LG2026
ConfHit: Conformal Generative Design with Oracle Free Guarantees
Siddhartha Laghuvarapu, Ying Jin, Jimeng Sun
The success of deep generative models in scientific discovery requires not only the ability to generate novel candidates but also reliable guarantees that these candidates indeed s…
cs.CL2025
InformGen: An AI Copilot for Accurate and Compliant Clinical Research Consent Document Generation
Zifeng Wang, Junyi Gao, Benjamin Danek +5
Leveraging large language models (LLMs) to generate high-stakes documents, such as informed consent forms (ICFs), remains a significant challenge due to the extreme need for regula…