91 citations · 581 across the 43 of their papers we have counts for
12 papers · 1 filter
Expertise-based Weighting for Regression Models with Noisy Labels
Milene Regina dos Santos, Rafael Izbicki
Regression methods assume that accurate labels are available for training. However, in certain scenarios, obtaining accurate labels may not be feasible, and relying on multiple spe…
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…
Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones
Trey McNeely, Galen Vincent, Rafael Izbicki +2
Tropical cyclone (TC) intensity forecasts are issued by human forecasters who evaluate spatio-temporal observations (e.g., satellite imagery) and model output (e.g., numerical weat…
CD-split and HPD-split: efficient conformal regions in high dimensions
Rafael Izbicki, Gilson Shimizu, Rafael B. Stern
Conformal methods create prediction bands that control average coverage assuming solely i.i.d. data. Although the literature has mostly focused on prediction intervals, more genera…
NLS: an accurate and yet easy-to-interpret regression method
Victor Coscrato, Marco Henrique de Almeida Inácio, Tiago Botari +1
An important feature of successful supervised machine learning applications is to be able to explain the predictions given by the regression or classification model being used. How…
Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders
Marco Henrique de Almeida Inácio, Rafael Izbicki, Bálint Gyires-Tóth
Given two distinct datasets, an important question is if they have arisen from the the same data generating function or alternatively how their data generating functions diverge fr…
Conditional independence testing: a predictive perspective
Marco Henrique de Almeida Inácio, Rafael Izbicki, Rafael Bassi Stern
Conditional independence testing is a key problem required by many machine learning and statistics tools. In particular, it is one way of evaluating the usefulness of some features…
Quantification under prior probability shift: the ratio estimator and its extensions
Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern
The quantification problem consists of determining the prevalence of a given label in a target population. However, one often has access to the labels in a sample from the training…