Machine Learning

Epistemic Uncertainty in Conformal Scores: A Unified Approach
WE introduce EPICSCORE, a model-agnostic method that enhances conformal prediction by integrating epistemic uncertainty. Compatible with any Bayesian model and maintaining distribution-free guarantees, EPICSCORE adapts prediction intervals based on data availability, achieving both finite-sample marginal and asymptotic conditional coverage.
L. M. C. Cabezas, V. S. Santos, T. R. Ramos, Rafael Izbicki
May, 2025 Proceedings of Machine Learning Research (UAI Track; Oral Presentation)
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Epistemic Uncertainty in Conformal Scores: A Unified Approach
CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference
L. M. C. Cabezas, V. S. Santos, T. R. Ramos, P. L. C. Rodrigues, Rafael Izbicki
March, 2025 Philosophical Transactions of the Royal Society A
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Towards instance-wise calibration: Local amortized diagnostics and reshaping of conditional densities (LADaR)
B. Dey, D. Zhao, B. Andrews, J. Newman, Rafael Izbicki, A. Lee
March, 2025 Machine Learning: Science and Technology
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PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification
A. Fröhlich, T. Ramos, G. Cabello, I. Buzatto, Rafael Izbicki, D. Tiezzi
February, 2025 In AAAI
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Regression Trees for Fast and Adaptive Prediction Intervals
L. M. C. Cabezas, M. P. Otto, Rafael Izbicki, R. B. Stern
February, 2025 Information Sciences
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Toward the End-to-End Optimization of the SWGO Array Layout
T. Dorigo, M. Aehle, C. Arcaro, M. Awais, F. Bergamaschi, J. Doniti, M. Doro, N. R. Gauger, Rafael Izbicki, J. Kieseler A. B. Lee, L. Masserano, F. Nardi, R. Rajesh, L. R. Vergara, A. Shen
January, 2025 Nuclear Physics B
Nonparametric quantification of uncertainty in multistep upscaling approaches: a case study on estimating forest biomass in the Brazilian Amazon
D. Valle, L. Haneda, Rafael Izbicki, R. Kamimura, B. Azevedo, S. Gomes, A. Sanchez, D. Almeida
January, 2025 Science Of Remote Sensing
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On the utility function of experiments in fundamental science
T. Dorigo, M. Doro, M. Aehle, M. Awais, N. R. Gauger, Rafael Izbicki, J. Kieseler A. B. Lee, L. Masserano, F. Nardi, A. Shen, L. R. Vergara
January, 2025 Physics Open
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Dengue nowcasting in Brazil by combining official surveillance data and Google Trends information
Y. Xiao, G. Soares, L. Bastos, Rafael Izbicki, P. Moraga
January, 2025 Plos One Neglected Tropical Diseases
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Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
We introduce Likelihood-Free Frequentist Inference (LF2I), a framework that bridges classical statistics and machine learning for valid confidence sets in complex, likelihood-free settings. LF2I provides confidence sets with near finite-sample validity and offers practical diagnostics for empirical coverage, ensuring reliable scientific inference without costly Monte Carlo or bootstrap methods.
N. Dalmasso, L. Masserano, D. Zhao, Rafael Izbicki, A. B. Lee
June, 2024 Electronic Journal of Statistics
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Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference