2 citations · 2 across the 5 of their papers we have counts for
7 papers
Targeted Maximum Likelihood Estimation for Integral Projection Models in Population Ecology
Yunzhe Zhou, Giles Hooker
Integral projection models (IPMs) are widely used to study population growth and the dynamics of demographic structure (e.g. age and size distributions) within a population.These m…
Targeted Learning for Variable Importance
Xiaohan Wang, Yunzhe Zhou, Giles Hooker
Variable importance is one of the most widely used measures for interpreting machine learning with significant interest from both statistics and machine learning communities. Recen…
Testing for the Markov Property in Time Series via Deep Conditional Generative Learning
Yunzhe Zhou, Chengchun Shi, Lexin Li +1
The Markov property is widely imposed in analysis of time series data. Correspondingly, testing the Markov property, and relatedly, inferring the order of a Markov model, are of pa…
A Generic Approach for Reproducible Model Distillation
Yunzhe Zhou, Peiru Xu, Giles Hooker
Model distillation has been a popular method for producing interpretable machine learning. It uses an interpretable "student" model to mimic the predictions made by the black box "…
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach
Yunzhe Zhou, Zhengling Qi, Chengchun Shi +1
In this article, we propose a novel pessimism-based Bayesian learning method for optimal dynamic treatment regimes in the offline setting. When the coverage condition does not hold…
The Infinitesimal Jackknife and Combinations of Models
Indrayudh Ghosal, Yunzhe Zhou, Giles Hooker
The Infinitesimal Jackknife is a general method for estimating variances of parametric models, and more recently also for some ensemble methods. In this paper we extend the Infinit…
Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning
Chengchun Shi, Yunzhe Zhou, Lexin Li
In this article, we propose a new hypothesis testing method for directed acyclic graph (DAG). While there is a rich class of DAG estimation methods, there is a relative paucity of…