1 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning
John Wu, Yongda Fan, Zhenbang Wu +14
Difficulty replicating baselines, high computational costs, and required domain expertise create persistent barriers to clinical AI research. To address these challenges, we introd…