Rafael Izbicki

Latest

• Trustworthy scientific inference for inverse problems with generative models
• On Focusing Statistical Power for Searches and Measurements in Particle Physics
• Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
• Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
• Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
• Toward the end-to-end optimization of the SWGO array layout
• Conditionally Calibrated Predictive Distributions by Probability-Probability Map: Application to Galaxy Redshift Estimation and Probabilistic Forecasting
• Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems
• Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems
• Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery
• Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery
• Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
• Diagnostics for Conditional Density Models and Bayesian Inference Algorithms
• Diagnostics for Conditional Density Models and Bayesian Inference Algorithms
• Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
• Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
• Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
• Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
• ABC-CDE: Toward Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
• Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation
• A unified framework for constructing, tuning and assessing photometric redshift density estimates in a selection bias setting
• Photo-z Estimation: An Example of Nonparametric Conditional Density Estimation under Selection Bias
• Nonparametric Conditional Density Estimation in a High-Dimensional Regression Setting.
• A Spectral Series Approach to High-Dimensional Nonparametric Regression
• High-Dimensional density ratio estimation with extensions to approximate likelihood computation