Testing for the Markov Property in Time Series via Deep Conditional Generative Learning

1 code implementation • 30 May 2023 • Yunzhe Zhou, Chengchun Shi, Lexin Li, Qiwei Yao

In this article, we propose a nonparametric test for the Markov property in high-dimensional time series via deep conditional generative learning.

Time Series

A Generic Approach for Reproducible Model Distillation

1 code implementation • 22 Nov 2022 • Yunzhe Zhou, Peiru Xu, Giles Hooker

Model distillation has been a popular method for producing interpretable machine learning.

Interpretable Machine Learning Symbolic Regression

Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach

1 code implementation • 26 Oct 2022 • Yunzhe Zhou, Zhengling Qi, Chengchun Shi, Lexin Li

In this article, we propose a novel pessimism-based Bayesian learning method for optimal dynamic treatment regimes in the offline setting.

Thompson Sampling Variational Inference

The Infinitesimal Jackknife and Combinations of Models

1 code implementation • 31 Aug 2022 • Indrayudh Ghosal, Yunzhe Zhou, Giles Hooker

In this paper we extend the Infinitesimal Jackknife to estimate the covariance between any two models.

Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning

1 code implementation • 2 Jun 2021 • Chengchun Shi, Yunzhe Zhou, Lexin Li

In this article, we propose a new hypothesis testing method for directed acyclic graph (DAG).

Additive models