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Found 18 papers, 12 papers with code
Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different between train and target data.
Simulator-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems
We also illustrate how our approach can correct overly confident posterior regions computed with normalizing flows.
Conditionally Calibrated Predictive Distributions by Probability-Probability Map: Application to Galaxy Redshift Estimation and Probabilistic Forecasting
The same regression function morphs the misspecified PD to a re-calibrated PD for all $\mathbf{x}$.
Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery
We prove that type I error control is guaranteed as long as the distribution of the label series is well-estimated, which is made easier by the extensive historical data for binary TC event labels.
Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones
Tropical cyclone (TC) intensity forecasts are issued by human forecasters who evaluate spatio-temporal observations (e. g., satellite imagery) and model output (e. g., numerical weather prediction, statistical models) to produce forecasts every 6 hours.
Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
In this work, we propose a unified and modular inference framework that bridges classical statistics and modern machine learning providing (i) a practical approach to the Neyman construction of confidence sets with frequentist finite-sample coverage for any value of the unknown parameters; and (ii) interpretable diagnostics that estimate the empirical coverage across the entire parameter space.
Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management
Prescribed burns are currently the most effective method of reducing the risk of widespread wildfires, but a largely missing component in forest management is knowing which fuels one can safely burn to minimize exposure to toxic smoke.
HECT: High-Dimensional Ensemble Consistency Testing for Climate Models
Climate models play a crucial role in understanding the effect of environmental and man-made changes on climate to help mitigate climate risks and inform governmental decisions.
Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning
Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters.
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
In this paper, we present $\texttt{ACORE}$ (Approximate Computation via Odds Ratio Estimation), a frequentist approach to LFI that first formulates the classical likelihood ratio test (LRT) as a parametrized classification problem, and then uses the equivalence of tests and confidence sets to build confidence regions for parameters of interest.
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
We provide sample code in $\texttt{Python}$ and $\texttt{R}$ as well as examples of applications to photometric redshift estimation and likelihood-free cosmological inference via CDE.
(f)RFCDE: Random Forests for Conditional Density Estimation and Functional Data
Furthermore, in settings with heteroskedasticity or multimodality, a regression point estimate with standard errors do not fully capture the uncertainty in our predictions.
Computation Methodology
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
At the heart of our approach is a two-sample test that quantifies the quality of the fit at fixed parameter values, and a global test that assesses goodness-of-fit across simulation parameters.
ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
Approximate Bayesian Computation (ABC) is typically used when the likelihood is either unavailable or intractable but where data can be simulated under different parameter settings using a forward model.
RFCDE: Random Forests for Conditional Density Estimation
Random forests is a common non-parametric regression technique which performs well for mixed-type data and irrelevant covariates, while being robust to monotonic variable transformations.
Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation
There is a growing demand for nonparametric conditional density estimators (CDEs) in fields such as astronomy and economics.
A Spectral Series Approach to High-Dimensional Nonparametric Regression
We expand the unknown regression on the data in terms of the eigenfunctions of a kernel-based operator, and we take advantage of orthogonality of the basis with respect to the underlying data distribution, P, to speed up computations and tuning of parameters.
Treelets--An adaptive multi-scale basis for sparse unordered data
In many modern applications, including analysis of gene expression and text documents, the data are noisy, high-dimensional, and unordered--with no particular meaning to the given order of the variables.
Methodology
