6 papers
LOCUS: A Distribution-Free Loss-Quantile Score for Risk-Aware Predictions
Matheus Barreto, Mário de Castro, Thiago R. Ramos +2
Modern machine learning models can be accurate on average yet still make mistakes that dominate deployment cost. We introduce Locus, a distribution-free wrapper that produces a per…
From Patents to Dataset: Scraping for Oxide Glass Compositions and Properties
Gustavo Laranja Thomaello, Thomaz Yeiden Busnardo Aguena, Eric Trevelato Costa +4
In this work, we present web scraping techniques to extract in- formation from patent tables, clean and structure them for future use in predictive machine learning models to devel…
Conformal Prediction for Compositional Data
Lucas P. Amaral, Luben M. C. Cabezas, Thiago R. Ramos +1
Dirichlet regression models are suitable for compositional data, in which the response variable represents proportions that sum to one. However, there are still no well-established…
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…