270 citations · 1.4k across the 97 of their papers we have counts for
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Unsupervised Learning via Total Correlation Explanation
Greg Ver Steeg
Learning by children and animals occurs effortlessly and largely without obvious supervision. Successes in automating supervised learning have not translated to the more ambiguous…
Fast structure learning with modular regularization
Greg Ver Steeg, Hrayr Harutyunyan, Daniel Moyer +1
Estimating graphical model structure from high-dimensional and undersampled data is a fundamental problem in many scientific fields. Existing approaches, such as GLASSO, latent var…
Toward Interpretable Topic Discovery via Anchored Correlation Explanation
Kyle Reing, David C. Kale, Greg Ver Steeg +1
Many predictive tasks, such as diagnosing a patient based on their medical chart, are ultimately defined by the decisions of human experts. Unfortunately, encoding experts' knowled…
Variational Information Maximization for Feature Selection
Shuyang Gao, Greg Ver Steeg, Aram Galstyan
Feature selection is one of the most fundamental problems in machine learning. An extensive body of work on information-theoretic feature selection exists which is based on maximiz…
Sifting Common Information from Many Variables
Greg Ver Steeg, Shuyang Gao, Kyle Reing +1
Measuring the relationship between any pair of variables is a rich and active area of research that is central to scientific practice. In contrast, characterizing the common inform…
The Information Sieve
Greg Ver Steeg, Aram Galstyan
We introduce a new framework for unsupervised learning of representations based on a novel hierarchical decomposition of information. Intuitively, data is passed through a series o…