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This paper introduces a cross-model map for certifying selective predictors that must meet both an automation floor and a risk ceiling under covariate shift, deriving feasibility frontiers and two-resource sample-complexity trade-offs.
This paper proposes a generalized distribution-free semi-supervised learning framework that constructs unbiased risk estimators via linear combinations of component risks, extending PNU learning to multiclass classification while achieving lower variance and providing generalization bounds.
Introduces a conformal prediction method for ordinal classification using the ranked probability score as a nonconformity function, producing median-centered contiguous prediction sets and achieving favorable balance between set width and ordinal miscoverage.
Proposes the first application of split conformal prediction to neural operator-based physics simulation, providing distribution-free prediction intervals with finite-sample coverage guarantees and adaptive-width intervals using MC Dropout uncertainty.
This paper presents LiverRisk, a machine learning framework for NAFLD risk prediction that combines gradient-boosted decision trees with conformal prediction to provide calibrated, distribution-free coverage guarantees on individual risk estimates, achieving high AUROC on internal and external cohorts.
Introduces Conf-Gen, a framework adapting conformal risk control to generative models, providing formal uncertainty guarantees for LLMs, image generators, and AI agents.
PASC proposes a conformal prediction method for multi-stage NLP and LLM pipelines that provides finite-sample, distribution-free joint coverage guarantees across all stages, achieving higher empirical coverage and efficiency than baselines like Bonferroni and independent CP.