91 citations · 581 across the 43 of their papers we have counts for
47 papers
Spectral smooth tests for goodness-of-fit
Victor Candido Reis, Rafael Izbicki
Goodness-of-fit tests are crucial tools for assessing the validity of statistical models. In this paper, we introduce a novel approach, the Spectral Smooth Test (SST), that general…
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…
Is augmentation effective to improve prediction in imbalanced text datasets?
Gabriel O. Assunção, Rafael Izbicki, Marcos O. Prates
Imbalanced datasets present a significant challenge for machine learning models, often leading to biased predictions. To address this issue, data augmentation techniques are widely…
Flexible conditional density estimation for time series
Gustavo Grivol, Rafael Izbicki, Alex A. Okuno +1
This paper introduces FlexCodeTS, a new conditional density estimator for time series. FlexCodeTS is a flexible nonparametric conditional density estimator, which can be based on a…
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…
The e-value and the Full Bayesian Significance Test: Logical Properties and Philosophical Consequences
Julio Michael Stern, Carlos Alberto de Braganca Pereira, Marcelo de Souza Lauretto +5
This article gives a conceptual review of the e-value, ev(H|X) -- the epistemic value of hypothesis H given observations X. This statistical significance measure was developed in o…
Logical coherence in Bayesian simultaneous three-way hypothesis tests
Bernardo F. Reimann, Rafael Izbicki, Julio M. Stern +2
This paper studies whether Bayesian simultaneous three-way hypothesis tests can be logically coherent. Two types of results are obtained. First, under the standard error-wise const…
A new LDA formulation with covariates
Gilson Shimizu, Rafael Izbicki, Denis Valle
The Latent Dirichlet Allocation (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed for text analysis, LDA has been use…
Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery
Trey McNeely, Galen Vincent, Kimberly M. Wood +2
Our goal is to quantify whether, and if so how, spatio-temporal patterns in tropical cyclone (TC) satellite imagery signal an upcoming rapid intensity change event. To address this…
Hierarchical clustering: visualization, feature importance and model selection
Luben M. C. Cabezas, Rafael Izbicki, Rafael B. Stern
We propose methods for the analysis of hierarchical clustering that fully use the multi-resolution structure provided by a dendrogram. Specifically, we propose a loss for choosing…
Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regression
Biprateep Dey, Jeffrey A. Newman, Brett H. Andrews +5
Many astrophysical analyses depend on estimates of redshifts (a proxy for distance) determined from photometric (i.e., imaging) data alone. Inaccurate estimates of photometric reds…
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…
Diagnostics for Conditional Density Models and Bayesian Inference Algorithms
David Zhao, Niccolò Dalmasso, Rafael Izbicki +1
There has been growing interest in the AI community for precise uncertainty quantification. Conditional density models f(y|x), where x represents potentially high-dimensional featu…
MeLIME: Meaningful Local Explanation for Machine Learning Models
Tiago Botari, Frederik Hvilshøj, Rafael Izbicki +1
Most state-of-the-art machine learning algorithms induce black-box models, preventing their application in many sensitive domains. Hence, many methodologies for explaining machine…
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…
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee
Parameter estimation, statistical tests and confidence sets are the cornerstones of classical statistics that allow scientists to make inferences about the underlying process that…
Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)
S. J. Schmidt, A. I. Malz, J. Y. H. Soo +24
Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) poster…
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…
Flexible distribution-free conditional predictive bands using density estimators
Rafael Izbicki, Gilson T. Shimizu, Rafael B. Stern
Conformal methods create prediction bands that control average coverage under no assumptions besides i.i.d. data. Besides average coverage, one might also desire to control conditi…
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…