80 citations · 548 across the 37 of their papers we have counts for
11 papers · 1 filter
Diagnostics for Conditional Density Models and Bayesian Inference Algorithms
David Zhao, Niccolò Dalmasso, Rafael Izbicki +1
There has been growing interest in the AI community for precise uncertainty quantification. Conditional density models f(y|x), where x represents potentially high-dimensional featu…
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee
Parameter estimation, statistical tests and confidence sets are the cornerstones of classical statistics that allow scientists to make inferences about the underlying process that…
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
Niccolò Dalmasso, Ann B. Lee, Rafael Izbicki +3
Complex phenomena in engineering and the sciences are often modeled with computationally intensive feed-forward simulations for which a tractable analytic likelihood does not exist…
Global and Local Two-Sample Tests via Regression
Ilmun Kim, Ann B. Lee, Jing Lei
Two-sample testing is a fundamental problem in statistics. Despite its long history, there has been renewed interest in this problem with the advent of high-dimensional and complex…
ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
Rafael Izbicki, Ann B. Lee, Taylor Pospisil
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…
Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation
Rafael Izbicki, Ann B. Lee
There is a growing demand for nonparametric conditional density estimators (CDEs) in fields such as astronomy and economics. In astronomy, for example, one can dramatically improve…