1 citations · 2 across the 9 of their papers we have counts for
11 papers
ReCIRC: Rectified Conformal Risk Control
Bruno Marcondes e Resende, Helton Graziadei, Thiago Rodrigo Ramos +1
Many applications of black-box predictive models require controlling task-relevant error rates, such as missed lesion pixels in segmentation or missed labels in multilabel classifi…
A Three-Way Testing Framework for Quantifying Epistemic Calibration Uncertainty in SBI
Luben M. C. Cabezas, Pedro L. C. Rodrigues, Rafael Izbicki
Current experimental scientists increasingly rely on simulation-based inference (SBI) to invert complex models with intractable likelihoods. A primary goal in these settings is to…
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…
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…
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…
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…
Conformal Calibration of Statistical Confidence Sets
Luben M. C. Cabezas, Guilherme P. Soares, Thiago R. Ramos +2
Constructing valid confidence sets is a crucial task in statistical inference, yet traditional methods often face challenges when dealing with complex models or limited observed sa…
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification
Alek Fröhlich, Thiago Ramos, Gustavo Cabello +3
Correctly assessing the malignancy of breast lesions identified during ultrasound examinations is crucial for effective clinical decision-making. However, the current "golden stand…
PROTEST: Nonparametric Testing of Hypotheses Enhanced by Experts' Utility Judgements
Rodrigo F. L. Lassance, Rafael Izbicki, Rafael B. Stern
Instead of testing solely a precise hypothesis, it is often useful to enlarge it with alternatives that are deemed to differ from it negligibly. For instance, in a bioequivalence s…
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…
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes
Victor Dheur, Tanguy Bosser, Rafael Izbicki +1
Sequences of labeled events observed at irregular intervals in continuous time are ubiquitous across various fields. Temporal Point Processes (TPPs) provide a mathematical framewor…