Rafael Izbicki | PhD
Rafael Izbicki | PhD
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machine learning
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
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)
PDF
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
NLS: 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)
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
PDF
Cite
Code
Likelihood-Free Frequentist Inference: Confidence Sets with Correct Conditional Coverage
Many areas of science make extensive use of computer simulators that implicitly encode likelihood functions of complex systems. …
N. Dalmasso
,
L. Masserano
,
D. Zhao
,
Rafael Izbicki
,
A. B. Lee
April, 2022
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