91 citations · 581 across the 43 of their papers we have counts for
5 papers · 2 filters
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…
Model interpretation using improved local regression with variable importance
Gilson Y. Shimizu, Rafael Izbicki, Andre C. P. L. F. de Carvalho
A fundamental question on the use of ML models concerns the explanation of their predictions for increasing transparency in decision-making. Although several interpretability metho…
Simulator-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems
Luca Masserano, Tommaso Dorigo, Rafael Izbicki +2
Prediction algorithms, such as deep neural networks (DNNs), are used in many domain sciences to directly estimate internal parameters of interest in simulator-based models, especia…
Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR)
Biprateep Dey, David Zhao, Brett H. Andrews +3
Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variable given complex inp…
A unified framework for dataset shift diagnostics
Felipe Maia Polo, Rafael Izbicki, Evanildo Gomes Lacerda +2
Supervised learning techniques typically assume training data originates from the target population. Yet, in reality, dataset shift frequently arises, which, if not adequately take…