Ann B. Lee

Latest

• 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
• Structural Forecasting for Short-Term Tropical Cyclone Intensity Guidance
• 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
• Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones
• Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning
• Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management
• HECT: High-Dimensional Ensemble Consistency Testing for Climate Models
• Unlocking GOES: A Statistical Framework for Quantifying the Evolution of Convective Structure in Tropical Cyclones
• 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
• Global and Local Two-Sample Tests via Regression
• (f)RFCDE: Random Forests for Conditional Density Estimation and Functional Data
• Non-Gaussianity in the Weak Lensing Correlation Function Likelihood - Implications for Cosmological Parameter Biases
• ABC-CDE: Toward Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
• Local Two-Sample Testing: A New Tool for Analysing High-Dimensional Astronomical Data
• 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
• Refining genetically inferred relationships using treelet covariance smoothing