Learning with many experts: Model selection and sparsity. - Rafael Izbicki; Rafael Bassi Stern (Stat. Anal. Data Min., 2013)

High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation. - Rafael Izbicki; Ann B. Lee; Chad Schafer (AISTATS, 2014)

A Bayesian Decision-Theoretic Approach to Logically-Consistent Hypothesis Testing. - Gustavo Miranda da Silva; Luís Gustavo Esteves; Victor Fossaluza; Rafael Izbicki; Sergio Wechsler (Entropy, 2015)

Logical consistency in simultaneous statistical test procedures. - Rafael Izbicki; Luís Gustavo Esteves (Log. J. IGPL, 2015)

The Logical Consistency of Simultaneous Agnostic Hypothesis Tests. - Luís Gustavo Esteves; Rafael Izbicki; Julio Michael Stern; Rafael Bassi Stern (Entropy, 2016)

Logically-consistent hypothesis testing and the hexagon of oppositions. - Julio Michael Stern; Rafael Izbicki; Luís Gustavo Esteves; Rafael Bassi Stern (Log. J. IGPL, 2017)

Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space. - Tiago Botari; Rafael Izbicki; André C. P. L. F. de Carvalho (PKDD/ECML Workshops (1), 2019)

Conditional independence testing: a predictive perspective. - Marco Henrique de Almeida Inácio; Rafael Izbicki; Rafael Bassi Stern (CoRR, 2019)

Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders. - Marco Henrique de Almeida Inácio; Rafael Izbicki; Bálint Gyires-Tóth (CoRR, 2019)

NLS: an accurate and yet easy-to-interpret regression method. - Victor Coscrato; Marco Henrique de Almeida Inácio; Tiago Botari; Rafael Izbicki (CoRR, 2019)

Distribution-free conditional predictive bands using density estimators. - Rafael Izbicki; Gilson Y. Shimizu; Rafael Bassi Stern (CoRR, 2019)

Pragmatic Hypotheses in the Evolution of Science. - Luís Gustavo Esteves; Rafael Izbicki; Julio Michael Stern; Rafael Bassi Stern (Entropy, 2019)

Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions. - Afonso Fernandes Vaz; Rafael Izbicki; Rafael Bassi Stern (J. Mach. Learn. Res., 2019)

Comparing probabilistic predictive models applied to football. - Márcio Alves Diniz; Rafael Izbicki; Danilo Lopes; Luis Ernesto Salasar (J. Oper. Res. Soc., 2019)

Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations. - Niccolò Dalmasso; Ann B. Lee; Rafael Izbicki; Taylor Pospisil; Ilmun Kim; Chieh-An Lin (AISTATS, 2020)

Flexible distribution-free conditional predictive bands using density estimators. - Rafael Izbicki; Gilson Y. Shimizu; Rafael Bassi Stern (AISTATS, 2020)

Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. - Niccolò Dalmasso; Rafael Izbicki; Ann B. Lee (ICML, 2020)

Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference. - Niccolò Dalmasso; Taylor Pospisil; Ann B. Lee; Rafael Izbicki; Peter E. Freeman; Alex I. Malz (Astron. Comput., 2020)

CD-split: efficient conformal regions in high dimensions. - Rafael Izbicki; Gilson Y. Shimizu; Rafael Bassi Stern (CoRR, 2020)

MeLIME: Meaningful Local Explanation for Machine Learning Models. - Tiago Botari; Frederik Hvilshøj; Rafael Izbicki; André C. P. L. F. de Carvalho (CoRR, 2020)

Comparing two populations using Bayesian Fourier series density estimation. - Marco Henrique de Almeida Inácio; Rafael Izbicki; Luis Ernesto Salasar (Commun. Stat. Simul. Comput., 2020)

The NN-Stacking: Feature weighted linear stacking through neural networks. - Victor Coscrato; Marco Henrique de Almeida Inácio; Rafael Izbicki (Neurocomputing, 2020)

Diagnostics for conditional density models and Bayesian inference algorithms. - David Zhao; Niccolò Dalmasso; Rafael Izbicki; Ann B. Lee (UAI, 2021)

Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning in Simulation and Uncertainty Quantification. - Niccolò Dalmasso; David Zhao; Rafael Izbicki; Ann B. Lee (CoRR, 2021)

Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones. - Trey McNeely; Galen Vincent; Rafael Izbicki; Kimberly M. Wood; Ann B. Lee (CoRR, 2021)

Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regression. - Biprateep Dey; Jeffrey A. Newman; Brett H. Andrews; Rafael Izbicki; Ann B. Lee; David Zhao; Markus Michael Rau; Alex I. Malz (CoRR, 2021)

Distance assessment and analysis of high-dimensional samples using variational autoencoders. - Marco Henrique de Almeida Inácio; Rafael Izbicki; Bálint Gyires-Tóth (Inf. Sci., 2021)

Calibrated Predictive Distributions via Diagnostics for Conditional Coverage. - Biprateep Dey; David Zhao; Jeffrey A. Newman; Brett H. Andrews; Rafael Izbicki; Ann B. Lee (CoRR, 2022)

Simulation-Based Inference with WALDO: Perfectly Calibrated Confidence Regions Using Any Prediction or Posterior Estimation Algorithm. - Luca Masserano; Tommaso Dorigo; Rafael Izbicki; Mikael Kuusela; Ann B. Lee (CoRR, 2022)

Model interpretation using improved local regression with variable importance. - Gilson Y. Shimizu; Rafael Izbicki; André C. P. L. F. de Carvalho (CoRR, 2022)

RFFNet: Scalable and interpretable kernel methods via Random Fourier Features. - Mateus P. Otto; Rafael Izbicki (CoRR, 2022)

CD-split and HPD-split: Efficient Conformal Regions in High Dimensions. - Rafael Izbicki; Gilson Y. Shimizu; Rafael Bassi Stern (J. Mach. Learn. Res., 2022)

Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems. - Luca Masserano; Tommaso Dorigo; Rafael Izbicki; Mikael Kuusela; Ann B. Lee (AISTATS, 2023)

Flexible conditional density estimation for time series. - Gustavo Grivol; Rafael Izbicki; Alex A. Okuno; Rafael Bassi Stern (CoRR, 2023)

Is augmentation effective to improve prediction in imbalanced text datasets? - Gabriel O. Assunção; Rafael Izbicki; Marcos O. Prates (CoRR, 2023)

Expertise-based Weighting for Regression Models with Noisy Labels. - Milene Regina dos Santos; Rafael Izbicki (CoRR, 2023)

Logical coherence in Bayesian simultaneous three-way hypothesis tests. - Luís Gustavo Esteves; Rafael Izbicki; Julio Michael Stern; Rafael Bassi Stern (Int. J. Approx. Reason., 2023)

NLS: An accurate and yet easy-to-interpret prediction method. - Victor Coscrato; Marco Henrique de Almeida Inácio; Tiago Botari; Rafael Izbicki (Neural Networks, 2023)

Hierarchical clustering: Visualization, feature importance and model selection. - Luben M. C. Cabezas; Rafael Izbicki; Rafael Bassi Stern (Appl. Soft Comput., 2023)

A unified framework for dataset shift diagnostics. - Felipe Maia Polo; Rafael Izbicki; Evanildo G. Lacerda Jr.; Juan Pablo Ibieta-Jimenez; Renato Vicente (Inf. Sci., 2023)

Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference. - Luca Masserano; Alexander Shen 003; Michele Doro; Tommaso Dorigo; Rafael Izbicki; Ann B. Lee (ICML, 2024)

Local uncertainty maps for land-use/land-cover classification without remote sensing and modeling work using a class-conditional conformal approach. - Denis Valle; Rodrigo Vieira Leite; Rafael Izbicki; Carlos Silva; Leo Haneda (Int. J. Appl. Earth Obs. Geoinformation, 2024)

PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification. - Alek Fröhlich; Thiago Ramos; Gustavo Cabello; Isabela Buzatto; Rafael Izbicki; Daniel Guimarães Tiezzi (CoRR, 2024)

Distribution-free conformal joint prediction regions for neural marked temporal point processes. - Victor Dheur; Tanguy Bosser; Rafael Izbicki; Souhaib Ben Taieb (Mach. Learn., 2024)

A new LDA formulation with covariates. - Gilson Y. Shimizu; Rafael Izbicki; Denis Valle (Commun. Stat. Simul. Comput., 2024)

PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification. - Alek Fröhlich; Thiago Ramos; Gustavo Motta Cabello Dos Santos; Isabela Panzeri Carlotti Buzatto; Rafael Izbicki; Daniel Guimarães Tiezzi (AAAI, 2025)

Epistemic Uncertainty in Conformal Scores: A Unified Approach. - Luben M. C. Cabezas; Vagner S. Santos; Thiago Ramos; Rafael Izbicki (UAI, 2025)

Adding imprecision to hypotheses: A Bayesian framework for testing practical significance in nonparametric settings. - Rodrigo F. L. Lassance; Rafael Izbicki; Rafael Bassi Stern (Int. J. Approx. Reason., 2025)

Regression trees for fast and adaptive prediction intervals. - Luben M. C. Cabezas; Mateus P. Otto; Rafael Izbicki; Rafael Bassi Stern (Inf. Sci., 2025)