9 papers
Conformal Prediction with Stable Measurement Mechanisms
Seungjin Choi
In applications such as air-quality monitoring, inexpensive sensor readings can help predict pollutant concentrations measured by a separate reference instrument. We study how to a…
Conformal Bayes under Continuous Label Shift: Sensitivity Analysis and the Limits of Exact Validity
Seungjin Choi
Conformal Bayes combines Bayesian posterior predictive scores with conformal calibration, but under continuous label shift both the score and calibration weight depend on the unkno…
Conformal Prediction for Molecular Properties under Label Shift
Hyeonsu Lee, Juyeon Kim, Erkhembayar Jadamba +2
Drug discovery and development underpins healthcare but remains costly and failure-prone. A critical bottleneck lies in predicting molecular properties such as solubility, potency,…
Anytime-Valid Evidence for Prespecified Predictive Corrections
Seungjin Choi
A predictive correction is a prespecified modification of an existing predictive distribution intended to reflect an anticipated change in future outcomes given their inputs, motiv…
Anytime-Valid Confirmation of Covariate Balance for Prespecified Corrections
Seungjin Choi
Many covariate-shift adaptation methods construct a correction , but users must still determine whether the corrected distribution is sufficiently balanced for the target str…
Conformal Bayes for Two-Sided Censored Gaussian Regression under Label Shift
Seungjin Choi
Prediction under label shift becomes nonstandard when responses are censored. In a two-sided censored Gaussian model, latent values below and above are recorded at the boun…
Conformal Bayes under Label Shift: Post-Hoc Calibration vs. In-Training Adaptation
Seungjin Choi
Conformal Bayes combines Bayesian posterior predictives with conformal calibration to produce prediction sets that are both statistically valid and geometrically efficient. We stud…
Conformal Candidate Certification for Offline Model-Based Optimization
Seungjin Choi
Offline model-based optimization (MBO) proposes candidates by optimizing a surrogate trained on a fixed historical dataset. Because candidates are deliberately out-of-distribution,…
Anytime-Valid Confirmation of Label-Shift Corrections
Seungjin Choi
In small-batch scientific deployments, labeled target outcomes may be too scarce for reliable shift estimation even when unlabeled target inputs are available. We address the compl…