4 citations · 5 across the 9 of their papers we have counts for
12 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…
Learning Conditional Expectation Operators via Functional Newton Updates
Thiago Ramos, Alek Fröhlich, Daniel Perazzo +1
We introduce the Functional Spectral-Newton Method (FSNM) for learning the leading singular structure of a conditional expectation operator without fixing a basis or reproducing ke…
Conformal Prediction via Transported Beta Laws
Thiago R. Ramos, Helton Graziadei, Luben M. C. Cabezas
Split conformal prediction provides finite-sample marginal coverage under exchangeability, but this guarantee averages over the random calibration sample. We study instead the law…
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…
LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees
Vagner Santos, Victor Coscrato, Luben Cabezas +2
Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify uncertainty. Conformal predicti…
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…
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…
Split Conformal Prediction and Non-Exchangeable Data
Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos +1
Split conformal prediction (CP) is arguably the most popular CP method for uncertainty quantification, enjoying both academic interest and widespread deployment. However, the origi…