Probabilistic learning of boolean functions applied to the binary classification problem with categorical covariates

降低分层分类变量中的维度与粒度

Reducing the dimensionality and granularity in hierarchical categorical variables

针对分层分类变量高维度高粒度易引发过拟合的问题，提出结合实体嵌入与自顶向下聚类的简化方法，可同时降维减粒度，经模拟与真实数据验证，能改善模型拟合与复杂度平衡且效果优于现有方案。

lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data

Longitudinal study designs are indispensable for studying disease progression. Inferring covariate effects from longitudinal data, however, requires interpretable methods that can model complicated covariance structures and detect nonlinear effects of both categorical and continuous covariates, as well as their interactions. Detecting disease effects is hindered by the fact that they often occur rapidly near the disease initiation time, and this time point cannot be exactly observed. An additional challenge is that the effect magnitude can be heterogeneous over the subjects. We present lgpr, a…

Low-Rank Boolean Matrix Approximation by Integer Programming

Low-rank approximations of data matrices are an important dimensionality reduction tool in machine learning and regression analysis. We consider the case of categorical variables, where it can be formulated as the problem of finding low-rank approximations to Boolean matrices. In this paper we give what is to the best of our knowledge the first integer programming formulation that relies on only polynomially many variables and constraints, we discuss how to solve it computationally and report numerical tests on synthetic and real-world data.

Searching for significant patterns in stratified data

Significant pattern mining, the problem of finding itemsets that are significantly enriched in one class of objects, is statistically challenging, as the large space of candidate patterns leads to an enormous multiple testing problem. Recently, the concept of testability was proposed as one approach to correct for multiple testing in pattern mining while retaining statistical power. Still, these strategies based on testability do not allow one to condition the test of significance on the observed covariates, which severely limits its utility in biomedical applications. Here we propose a strate…

Imputation of Clinical Covariates in Time Series

Missing data is a common problem in real-world settings and particularly relevant in healthcare applications where researchers use Electronic Health Records (EHR) and results of observational studies to apply analytics methods. This issue becomes even more prominent for longitudinal data sets, where multiple instances of the same individual correspond to different observations in time. Standard imputation methods do not take into account patient specific information incorporated in multivariate panel data. We introduce the novel imputation algorithm MedImpute that addresses this problem, exten…

CardiCat：一种用于高基数表格数据的变分自编码器

CardiCat: a Variational Autoencoder for High-Cardinality Tabular Data

针对高基数混合类型表格数据生成难题，提出变分自编码器CardiCat，采用正则化双编码器-解码器嵌入层替代独热编码，实现紧凑参数化，在更小参数空间下生成高质量合成数据。