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.LG2026
Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification
Arjun Chatterjee, Sayeed Sajjad Razin, John Wu +3
Quantifying uncertainty in clinical predictions is critical for high-stakes diagnosis tasks. Conformal prediction offers a principled approach by providing prediction sets with the…