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20242026
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stat.ML2026

Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps

Elizabeth Cucuzzella, Rafael Izbicki, Ann B. Lee

Machine learning forecast systems are moving beyond point predictions to full predictive distributions for future outcomes y conditional on complex inputs x. However, these distrib…

stat.ML2026

CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference

Luben M. C. Cabezas, Vagner S. Santos, Thiago R. Ramos +2

Current experimental scientists have been increasingly relying on simulation-based inference (SBI) to invert complex non-linear models with intractable likelihoods. However, poster…

stat.ML2026

CREDO: Epistemic-Aware Conformalized Credal Envelopes for Regression

Luben M. C. Cabezas, Sabina J. Sloman, Bruno M. Resende +3

Conformal prediction delivers prediction intervals with distribution-free coverage, but its intervals can look overconfident in regions where the model is extrapolating, because st…

stat.ML2025

Epistemic Uncertainty in Conformal Scores: A Unified Approach

Luben M. C. Cabezas, Vagner S. Santos, Thiago R. Ramos +1

Conformal prediction methods create prediction bands with distribution-free guarantees but do not explicitly capture epistemic uncertainty, which can lead to overconfident predicti…

stat.ML2024

Regression Trees for Fast and Adaptive Prediction Intervals

Luben M. C. Cabezas, Mateus P. Otto, Rafael Izbicki +1

Predictive models make mistakes. Hence, there is a need to quantify the uncertainty associated with their predictions. Conformal inference has emerged as a powerful tool to create…