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stat.ML2024
Regression Trees for Fast and Adaptive Prediction Intervals
Luben M. C. Cabezas, Mateus P. Otto, Rafael Izbicki +1
Predictive models make mistakes. Hence, there is a need to quantify the uncertainty associated with their predictions. Conformal inference has emerged as a powerful tool to create…
stat.ML2022
RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features
Mateus P. Otto, Rafael Izbicki
Kernel methods provide a flexible and theoretically grounded approach to nonlinear and nonparametric learning. While memory and run-time requirements hinder their applicability to…