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Rafael Izbicki
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2020 – today
- 2025
- [j19]Luben M. C. Cabezas
, Mateus P. Otto
Regression trees for fast and adaptive prediction intervals. Inf. Sci. 686: 121369 (2025) - 2024
- [j18]Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb:
Distribution-free conformal joint prediction regions for neural marked temporal point processes. Mach. Learn. 113(9): 7055-7102 (2024) - [c8]Luca Masserano, Alexander Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee:
Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference. ICML 2024 - [i28]Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb:
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes. CoRR abs/2401.04612 (2024) - [i27]Luca Masserano, Alexander Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee:
Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference. CoRR abs/2402.05330 (2024) - [i26]Luben M. C. Cabezas, Mateus P. Otto, Rafael Izbicki, Rafael Bassi Stern:
Regression Trees for Fast and Adaptive Prediction Intervals. CoRR abs/2402.07357 (2024) - [i25]Alek Fröhlich, Thiago Ramos, Gustavo Cabello, Isabela Buzatto, Rafael Izbicki, Daniel Tiezzi:
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification. CoRR abs/2408.15458 (2024) - 2023
- [j17]Luben M. C. Cabezas
Hierarchical clustering: Visualization, feature importance and model selection. Appl. Soft Comput. 141: 110303 (2023) - [j16]Luís Gustavo Esteves, Rafael Izbicki
Logical coherence in Bayesian simultaneous three-way hypothesis tests. Int. J. Approx. Reason. 152: 297-309 (2023) - [j15]Felipe Maia Polo
A unified framework for dataset shift diagnostics. Inf. Sci. 649: 119612 (2023) - [j14]Victor Coscrato
NLS: An accurate and yet easy-to-interpret prediction method. Neural Networks 162: 117-130 (2023) - [c7]Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, Ann B. Lee:
Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems. AISTATS 2023: 2960-2974 - [i24]Gustavo Grivol, Rafael Izbicki
Flexible conditional density estimation for time series. CoRR abs/2301.09671 (2023) - [i23]Gabriel O. Assunção, Rafael Izbicki
Is augmentation effective to improve prediction in imbalanced text datasets? CoRR abs/2304.10283 (2023) - [i22]Milene Regina dos Santos, Rafael Izbicki:
Expertise-based Weighting for Regression Models with Noisy Labels. CoRR abs/2305.07430 (2023) - 2022
- [j13]Rafael Izbicki, Gilson Y. Shimizu, Rafael Bassi Stern:
CD-split and HPD-split: Efficient Conformal Regions in High Dimensions. J. Mach. Learn. Res. 23: 87:1-87:32 (2022) - [i21]Gilson Y. Shimizu, Rafael Izbicki, Denis Valle:
A new LDA formulation with covariates. CoRR abs/2202.11527 (2022) - [i20]Felipe Maia Polo, Rafael Izbicki
A unified framework for dataset shift diagnostics. CoRR abs/2205.08340 (2022) - [i19]Biprateep Dey, David Zhao, Jeffrey A. Newman, Brett H. Andrews, Rafael Izbicki
Calibrated Predictive Distributions via Diagnostics for Conditional Coverage. CoRR abs/2205.14568 (2022) - [i18]Luca Masserano, Tommaso Dorigo, Rafael Izbicki
Simulation-Based Inference with WALDO: Perfectly Calibrated Confidence Regions Using Any Prediction or Posterior Estimation Algorithm. CoRR abs/2205.15680 (2022) - [i17]Gilson Y. Shimizu, Rafael Izbicki
Model interpretation using improved local regression with variable importance. CoRR abs/2209.05371 (2022) - [i16]Mateus P. Otto, Rafael Izbicki
RFFNet: Scalable and interpretable kernel methods via Random Fourier Features. CoRR abs/2211.06410 (2022) - 2021
- [j12]Marco Henrique de Almeida Inácio, Rafael Izbicki
Distance assessment and analysis of high-dimensional samples using variational autoencoders. Inf. Sci. 557: 407-420 (2021) - [c6]David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee:
Diagnostics for conditional density models and Bayesian inference algorithms. UAI 2021: 1830-1840 - [i15]Niccolò Dalmasso, David Zhao, Rafael Izbicki, Ann B. Lee:
Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning in Simulation and Uncertainty Quantification. CoRR abs/2107.03920 (2021) - [i14]Trey McNeely, Galen Vincent, Rafael Izbicki, Kimberly M. Wood, Ann B. Lee:
Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones. CoRR abs/2109.12029 (2021) - [i13]Biprateep Dey, Jeffrey A. Newman, Brett H. Andrews, Rafael Izbicki, Ann B. Lee, David Zhao, Markus Michael Rau, Alex I. Malz:
Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regression. CoRR abs/2110.15209 (2021) - [i12]Luben M. C. Cabezas, Rafael Izbicki, Rafael Bassi Stern:
Hierarchical clustering: visualization, feature importance and model selection. CoRR abs/2112.01372 (2021) - 2020
- [j11]Niccolò Dalmasso, Taylor Pospisil, Ann B. Lee, Rafael Izbicki
Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference. Astron. Comput. 30: 100362 (2020) - [j10]Marco Henrique de Almeida Inácio, Rafael Izbicki
Comparing two populations using Bayesian Fourier series density estimation. Commun. Stat. Simul. Comput. 49(1): 261-282 (2020) - [j9]Victor Coscrato, Marco Henrique de Almeida Inácio
The NN-Stacking: Feature weighted linear stacking through neural networks. Neurocomputing 399: 141-152 (2020) - [c5]Rafael Izbicki, Gilson Y. Shimizu, Rafael Bassi Stern:
Flexible distribution-free conditional predictive bands using density estimators. AISTATS 2020: 3068-3077 - [c4]Niccolò Dalmasso, Ann B. Lee, Rafael Izbicki, Taylor Pospisil, Ilmun Kim, Chieh-An Lin:
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations. AISTATS 2020: 3349-3361 - [c3]Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee:
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. ICML 2020: 2323-2334 - [i11]Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee:
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. CoRR abs/2002.10399 (2020) - [i10]Rafael Izbicki, Gilson Y. Shimizu, Rafael Bassi Stern:
CD-split: efficient conformal regions in high dimensions. CoRR abs/2007.12778 (2020) - [i9]Tiago Botari, Frederik Hvilshøj, Rafael Izbicki, André C. P. L. F. de Carvalho:
MeLIME: Meaningful Local Explanation for Machine Learning Models. CoRR abs/2009.05818 (2020)
2010 – 2019
- 2019
- [j8]Luís Gustavo Esteves, Rafael Izbicki
Pragmatic Hypotheses in the Evolution of Science. Entropy 21(9): 883 (2019) - [j7]Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern:
Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions. J. Mach. Learn. Res. 20: 79:1-79:33 (2019) - [j6]Márcio Alves Diniz, Rafael Izbicki
Comparing probabilistic predictive models applied to football. J. Oper. Res. Soc. 70(5): 770-782 (2019) - [c2]Tiago Botari
Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space. PKDD/ECML Workshops (1) 2019: 241-252 - [i8]Victor Coscrato, Marco Henrique de Almeida Inácio, Rafael Izbicki:
The NN-Stacking: Feature weighted linear stacking through neural networks. CoRR abs/1906.09735 (2019) - [i7]Tiago Botari, Rafael Izbicki, André C. P. L. F. de Carvalho:
Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space. CoRR abs/1907.13525 (2019) - [i6]Marco Henrique de Almeida Inácio, Rafael Izbicki, Rafael Bassi Stern:
Conditional independence testing: a predictive perspective. CoRR abs/1908.00105 (2019) - [i5]Marco Henrique de Almeida Inácio, Rafael Izbicki, Bálint Gyires-Tóth:
Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders. CoRR abs/1909.07182 (2019) - [i4]Victor Coscrato, Marco Henrique de Almeida Inácio, Tiago Botari, Rafael Izbicki:
NLS: an accurate and yet easy-to-interpret regression method. CoRR abs/1910.05206 (2019) - [i3]Rafael Izbicki, Gilson Y. Shimizu, Rafael Bassi Stern:
Distribution-free conditional predictive bands using density estimators. CoRR abs/1910.05575 (2019) - 2018
- [i2]Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern:
Quantification under prior probability shift: the ratio estimator and its extensions. CoRR abs/1807.03929 (2018) - 2017
- [j5]Julio Michael Stern
Logically-consistent hypothesis testing and the hexagon of oppositions. Log. J. IGPL 25(5): 741-757 (2017) - 2016
- [j4]Luís Gustavo Esteves, Rafael Izbicki
The Logical Consistency of Simultaneous Agnostic Hypothesis Tests. Entropy 18(7): 256 (2016) - 2015
- [j3]Gustavo Miranda da Silva, Luís Gustavo Esteves, Victor Fossaluza
A Bayesian Decision-Theoretic Approach to Logically-Consistent Hypothesis Testing. Entropy 17(10): 6534-6559 (2015) - [j2]Rafael Izbicki
Logical consistency in simultaneous statistical test procedures. Log. J. IGPL 23(5): 732-758 (2015) - 2014
- [c1]Rafael Izbicki, Ann B. Lee, Chad Schafer:
High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation. AISTATS 2014: 420-429 - [i1]Rafael Izbicki, Rafael Bassi Stern:
Learning with many experts: model selection and sparsity. CoRR abs/1405.3292 (2014) - 2013
- [j1]Rafael Izbicki
Learning with many experts: Model selection and sparsity. Stat. Anal. Data Min. 6(6): 565-577 (2013)
Coauthor Index
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last updated on 2024-10-01 20:41 CEST by the dblp team
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