3 papers
cs.LG2025
Supervised Stochastic Gradient Algorithms for Multi-Trial Source Separation
Ronak Mehta, Mateus Piovezan Otto, Noah Stanis +2
We develop a stochastic algorithm for independent component analysis that incorporates multi-trial supervision, which is available in many scientific contexts. The method blends a…
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…