Rafael Izbicki

70 papers A* 3A 6Journal 60Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Benchmarking Tabular Foundation Models for Conditional Density Estimation in Regression. CoRR Rafael Izbicki, Pedro L. C. Rodrigues
2026	J	`jnl`	CREDO: Epistemic-Aware Conformalized Credal Envelopes for Regression. CoRR Luben M. C. Cabezas, Sabina J. Sloman, Bruno M. Resende, Fanyi Wu, Michele Caprio, Rafael Izbicki
2026	J	`jnl`	Conformal Calibration of Statistical Confidence Sets. Trans. Mach. Learn. Res. Luben M. C. Cabezas, Guilherme Soares, Thiago R. Ramos, Rafael Bassi Stern, Rafael Izbicki
2026	J	`jnl`	LOCUS: A Distribution-Free Loss-Quantile Score for Risk-Aware Predictions. CoRR Matheus Barreto, Mário de Castro, Thiago R. Ramos, Denis Valle, Rafael Izbicki
2026	J	`jnl`	LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees. CoRR Vagner S. Santos, Victor Coscrato, Luben M. C. Cabezas, Rafael Izbicki, Thiago R. Ramos
2026	J	`jnl`	Trustworthy predictive distributions for rare events via diagnostic transport maps. CoRR Elizabeth Cucuzzella, Rafael Izbicki, Ann B. Lee
2025	J	`jnl`	Adding imprecision to hypotheses: A Bayesian framework for testing practical significance in nonparametric settings. Int. J. Approx. Reason. Rodrigo F. L. Lassance, Rafael Izbicki, Rafael Bassi Stern
2025	J	`jnl`	CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference. CoRR Luben M. C. Cabezas, Vagner S. Santos, Thiago R. Ramos, Pedro L. C. Rodrigues, Rafael Izbicki
2025	J	`jnl`	DiNo and RanBu: Lightweight Predictions from Shallow Random Forests. CoRR Tiago Mendonça dos Santos, Rafael Izbicki, Luís Gustavo Esteves
2025	A	`conf`	Epistemic Uncertainty in Conformal Scores: A Unified Approach. UAI Luben M. C. Cabezas, Vagner S. Santos, Thiago R. Ramos, Rafael Izbicki
2025	J	`jnl`	Epistemic Uncertainty in Conformal Scores: A Unified Approach. CoRR Luben M. C. Cabezas, Vagner S. Santos, Thiago R. Ramos, Rafael Izbicki
2025	A*	`conf`	PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification. AAAI Alek Fröhlich, Thiago R. Ramos, Gustavo Motta Cabello Dos Santos, Isabela Panzeri Carlotti Buzatto, Rafael Izbicki, Daniel Guimarães Tiezzi
2025	J	`jnl`	Regression trees for fast and adaptive prediction intervals. Inf. Sci. Luben M. C. Cabezas, Mateus P. Otto, Rafael Izbicki, Rafael Bassi Stern
2025	J	`jnl`	Towards instance-wise calibration: local amortized diagnostics and reshaping of conditional densities (LADaR). Mach. Learn. Sci. Technol. Biprateep Dey, David Zhao, Brett H. Andrews, Jeffrey A. Newman, Rafael Izbicki, Ann B. Lee
2024	J	`jnl`	A new LDA formulation with covariates. Commun. Stat. Simul. Comput. Gilson Y. Shimizu, Rafael Izbicki, Denis Valle
2024	A*	`conf`	Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference. ICML Luca Masserano, Alexander Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee
2024	J	`jnl`	Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference. CoRR Luca Masserano, Alexander Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee
2024	J	`jnl`	Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes. CoRR Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb
2024	J	`jnl`	Distribution-free conformal joint prediction regions for neural marked temporal point processes. Mach. Learn. Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb
2024	J	`jnl`	Local uncertainty maps for land-use/land-cover classification without remote sensing and modeling work using a class-conditional conformal approach. Int. J. Appl. Earth Obs. Geoinformation Denis Valle, Rodrigo Vieira Leite, Rafael Izbicki, Carlos Silva, Leo Haneda
2024	J	`jnl`	PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification. CoRR Alek Fröhlich, Thiago R. Ramos, Gustavo Cabello, Isabela Buzatto, Rafael Izbicki, Daniel Guimarães Tiezzi
2024	J	`jnl`	Regression Trees for Fast and Adaptive Prediction Intervals. CoRR Luben M. C. Cabezas, Mateus P. Otto, Rafael Izbicki, Rafael Bassi Stern
2023	J	`jnl`	A unified framework for dataset shift diagnostics. Inf. Sci. Felipe Maia Polo, Rafael Izbicki, Evanildo G. Lacerda Jr., Juan Pablo Ibieta-Jimenez, Renato Vicente
2023	J	`jnl`	Expertise-based Weighting for Regression Models with Noisy Labels. CoRR Milene Regina dos Santos, Rafael Izbicki
2023	J	`jnl`	Flexible conditional density estimation for time series. CoRR Gustavo Grivol, Rafael Izbicki, Alex A. Okuno, Rafael Bassi Stern
2023	J	`jnl`	Hierarchical clustering: Visualization, feature importance and model selection. Appl. Soft Comput. Luben M. C. Cabezas, Rafael Izbicki, Rafael Bassi Stern
2023	J	`jnl`	Is augmentation effective to improve prediction in imbalanced text datasets? CoRR Gabriel O. Assunção, Rafael Izbicki, Marcos O. Prates
2023	J	`jnl`	Logical coherence in Bayesian simultaneous three-way hypothesis tests. Int. J. Approx. Reason. Luís Gustavo Esteves, Rafael Izbicki, Julio Michael Stern, Rafael Bassi Stern
2023	J	`jnl`	NLS: An accurate and yet easy-to-interpret prediction method. Neural Networks Victor Coscrato, Marco Henrique de Almeida Inácio, Tiago Botari, Rafael Izbicki
2023	A	`conf`	Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems. AISTATS Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, Ann B. Lee
2022	J	`jnl`	A new LDA formulation with covariates. CoRR Gilson Y. Shimizu, Rafael Izbicki, Denis Valle
2022	J	`jnl`	A unified framework for dataset shift diagnostics. CoRR Felipe Maia Polo, Rafael Izbicki, Evanildo Gomes Lacerda Jr., Juan Pablo Ibieta-Jimenez, Renato Vicente
2022	J	`jnl`	CD-split and HPD-split: Efficient Conformal Regions in High Dimensions. J. Mach. Learn. Res. Rafael Izbicki, Gilson Y. Shimizu, Rafael Bassi Stern
2022	J	`jnl`	Calibrated Predictive Distributions via Diagnostics for Conditional Coverage. CoRR Biprateep Dey, David Zhao, Jeffrey A. Newman, Brett H. Andrews, Rafael Izbicki, Ann B. Lee
2022	J	`jnl`	Model interpretation using improved local regression with variable importance. CoRR Gilson Y. Shimizu, Rafael Izbicki, André C. P. L. F. de Carvalho
2022	J	`jnl`	RFFNet: Scalable and interpretable kernel methods via Random Fourier Features. CoRR Mateus P. Otto, Rafael Izbicki
2022	J	`jnl`	Simulation-Based Inference with WALDO: Perfectly Calibrated Confidence Regions Using Any Prediction or Posterior Estimation Algorithm. CoRR Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, Ann B. Lee
2021	A	`conf`	Diagnostics for conditional density models and Bayesian inference algorithms. UAI David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee
2021	J	`jnl`	Distance assessment and analysis of high-dimensional samples using variational autoencoders. Inf. Sci. Marco Henrique de Almeida Inácio, Rafael Izbicki, Bálint Gyires-Tóth
2021	J	`jnl`	Hierarchical clustering: visualization, feature importance and model selection. CoRR Luben M. C. Cabezas, Rafael Izbicki, Rafael Bassi Stern
2021	J	`jnl`	Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones. CoRR Trey McNeely, Galen Vincent, Rafael Izbicki, Kimberly M. Wood, Ann B. Lee
2021	J	`jnl`	Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning in Simulation and Uncertainty Quantification. CoRR Niccolò Dalmasso, David Zhao, Rafael Izbicki, Ann B. Lee
2021	J	`jnl`	Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regression. CoRR Biprateep Dey, Jeffrey A. Newman, Brett H. Andrews, Rafael Izbicki, Ann B. Lee, David Zhao, Markus Michael Rau, Alex I. Malz
2020	J	`jnl`	CD-split: efficient conformal regions in high dimensions. CoRR Rafael Izbicki, Gilson Y. Shimizu, Rafael Bassi Stern
2020	J	`jnl`	Comparing two populations using Bayesian Fourier series density estimation. Commun. Stat. Simul. Comput. Marco Henrique de Almeida Inácio, Rafael Izbicki, Luis Ernesto Salasar
2020	J	`jnl`	Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference. Astron. Comput. Niccolò Dalmasso, Taylor Pospisil, Ann B. Lee, Rafael Izbicki, Peter E. Freeman, Alex I. Malz
2020	A*	`conf`	Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. ICML Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee
2020	J	`jnl`	Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. CoRR Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee
2020	A	`conf`	Flexible distribution-free conditional predictive bands using density estimators. AISTATS Rafael Izbicki, Gilson Y. Shimizu, Rafael Bassi Stern
2020	J	`jnl`	MeLIME: Meaningful Local Explanation for Machine Learning Models. CoRR Tiago Botari, Frederik Hvilshøj, Rafael Izbicki, André C. P. L. F. de Carvalho
2020	J	`jnl`	The NN-Stacking: Feature weighted linear stacking through neural networks. Neurocomputing Victor Coscrato, Marco Henrique de Almeida Inácio, Rafael Izbicki
2020	A	`conf`	Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations. AISTATS Niccolò Dalmasso, Ann B. Lee, Rafael Izbicki, Taylor Pospisil, Ilmun Kim, Chieh-An Lin
2019	J	`jnl`	Comparing probabilistic predictive models applied to football. J. Oper. Res. Soc. Márcio Alves Diniz, Rafael Izbicki, Danilo Lopes, Luis Ernesto Salasar
2019	J	`jnl`	Conditional independence testing: a predictive perspective. CoRR Marco Henrique de Almeida Inácio, Rafael Izbicki, Rafael Bassi Stern
2019	J	`jnl`	Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders. CoRR Marco Henrique de Almeida Inácio, Rafael Izbicki, Bálint Gyires-Tóth
2019	J	`jnl`	Distribution-free conditional predictive bands using density estimators. CoRR Rafael Izbicki, Gilson Y. Shimizu, Rafael Bassi Stern
2019	—	`conf`	Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space. PKDD/ECML Workshops (1) Tiago Botari, Rafael Izbicki, André C. P. L. F. de Carvalho
2019	J	`jnl`	Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space. CoRR Tiago Botari, Rafael Izbicki, André C. P. L. F. de Carvalho
2019	J	`jnl`	NLS: an accurate and yet easy-to-interpret regression method. CoRR Victor Coscrato, Marco Henrique de Almeida Inácio, Tiago Botari, Rafael Izbicki
2019	J	`jnl`	Pragmatic Hypotheses in the Evolution of Science. Entropy Luís Gustavo Esteves, Rafael Izbicki, Julio Michael Stern, Rafael Bassi Stern
2019	J	`jnl`	Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions. J. Mach. Learn. Res. Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern
2019	J	`jnl`	The NN-Stacking: Feature weighted linear stacking through neural networks. CoRR Victor Coscrato, Marco Henrique de Almeida Inácio, Rafael Izbicki
2018	J	`jnl`	Quantification under prior probability shift: the ratio estimator and its extensions. CoRR Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern
2017	J	`jnl`	Logically-consistent hypothesis testing and the hexagon of oppositions. Log. J. IGPL Julio Michael Stern, Rafael Izbicki, Luís Gustavo Esteves, Rafael Bassi Stern
2016	J	`jnl`	The Logical Consistency of Simultaneous Agnostic Hypothesis Tests. Entropy Luís Gustavo Esteves, Rafael Izbicki, Julio Michael Stern, Rafael Bassi Stern
2015	J	`jnl`	A Bayesian Decision-Theoretic Approach to Logically-Consistent Hypothesis Testing. Entropy Gustavo Miranda da Silva, Luís Gustavo Esteves, Victor Fossaluza, Rafael Izbicki, Sergio Wechsler
2015	J	`jnl`	Logical consistency in simultaneous statistical test procedures. Log. J. IGPL Rafael Izbicki, Luís Gustavo Esteves
2014	A	`conf`	High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation. AISTATS Rafael Izbicki, Ann B. Lee, Chad Schafer
2014	J	`jnl`	Learning with many experts: model selection and sparsity. CoRR Rafael Izbicki, Rafael Bassi Stern
2013	J	`jnl`	Learning with many experts: Model selection and sparsity. Stat. Anal. Data Min. Rafael Izbicki, Rafael Bassi Stern

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.