Machine Learning

The Quasar Catalogue for S-PLUS DR4 (QuCatS) and the estimation of photometric redshifts
L. Nakazono, R. R. Valença, G. Soares, Rafael Izbicki, Ž. Ivezić
January, 2024 Monthly Notices of the Royal Astronomical Society
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Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery
T. McNeely, G. Vincente, K. M. Wood, Rafael Izbicki, A. B. Lee
August, 2023 Annals of Applied Statistics (Selected for The Best of AOAS invited paper session at JSM 2023)
Preprint PDF
Classification under Prior Probability Shift in Simulator-Based Inference: Application to Atmospheric Cosmic-Ray Showers
A. Shen, L. Masserano, Rafael Izbicki, T. Dorigo, M. Doro, A. B. Lee
March, 2023 NeurIPS (Machine Learning and the Physical Sciences Workshop; Best Poster Award)
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A unified framework for dataset shift diagnostics
F. M. Polo, Rafael Izbicki, E. G. Lacerda Jr, J. P. Ibieta-Jimenez, R. Vicente
March, 2023 Information Sciences
Preprint
Hierarchical clustering: visualization, feature importance and model selection
We propose methods for the analysis of hierarchical clustering that fully use the multi-resolution structure provided by a dendrogram. …
L. M. C. Cabezas, Rafael Izbicki, R. B. Stern
February, 2023 Applied Soft Computing Journal
Preprint
Simulation-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms or Posterior Estimators for Inverse Problems
L. Masserano, T. Dorigo, Rafael Izbicki, M. Kuusela, A. B. Lee
February, 2023 Proceedings of Machine Learning Research (AISTATS track; Finalist at the ASA SPES and Q&P Student Paper Competition)
Preprint PDF
Quantifying uncertainty in land-use land-cover classification using conformal statistics
D. Valle, Rafael Izbicki, R. Leite
February, 2023 Remote Sensing of Environment
NLS: An accurate and yet easy-to-interpret prediction method
Over the last years, the predictive power of supervised machine learning (ML) has undergone impressive advances, achieving the status …
V. Coscrato, M. H. A. Inacio, T. Botari, Rafael Izbicki
January, 2023 Neural Networks
Preprint PDF
A new LDA formulation with covariates
G. Shimizu, Rafael Izbicki, D. Valle
January, 2023 Communications in Statistics - Simulation and Computation
Preprint
CD-split and HPD-split: Efficient conformal regions in high dimensions
Conformal methods create prediction bands that control average coverage assuming solely i.i.d. data. We introduce CD-split and HPD-split, which yield general prediction regions and converge to the optimal highest predictive density set.
Rafael Izbicki, Gilson Shimizu, Rafael B. Stern
May, 2022 Journal of Machine Learning Research
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CD-split and HPD-split: Efficient conformal regions in high dimensions