Hierarchical probability models are widely used for data classified in a treelike structure and in Bayesian inference. The main characteristic of such models is to have the probability law at some level in the classification structure be conditional on the outcome in previous levels. For example, adopting a bottom to top description of the model, a simple hierarchical model could be written as ...

In the past decade, a number of advances in topic modeling have produced sophisticated models that are capable of generating hierarchies of topics. One challenge for these models is scalability: they are incapable of working at the massive scale of millions of documents and hundreds of thousands of terms. We address this challenge with a technique that learns a hierarchy of topics by iterativel...

A goal of central importance in the study of hierarchical models for object recognition – and indeed the mammalian visual cortex – is that of understanding quantitatively the trade-off between invariance and selectivity, and how invariance and discrimination properties contribute towards providing an improved representation useful for learning from data. In this work we provide a general group-...

Implicit probabilistic models are a flexible class of models defined by a simulation process for data. They form the basis for theories which encompass our understanding of the physical world. Despite this fundamental nature, the use of implicit models remains limited due to challenges in specifying complex latent structure in them, and in performing inferences in such models with large data se...

Federal law prohibits discrimination in employment decisions against persons in certain protected categories. The common method for measuring discrimination involves a comparison of some aggregate statistic for protected and non-protected individuals. This approach is open to question when employment decisions are made over an extended time period. We show how to use hierarchical proportional h...

As technology continues to scale and decrease in size, the reliability of transistors is becoming an ever increasing problem. Previous second order effects are becoming more pronounced, and other effects, reliability effects, as we shall see, are changing the operation of devices and can even cease to operate properly. The purpose of this project is to investigate device reliability from a hier...

Machine Learning techniques have been used quite widely for the task of predicting cognitive processes from fMRI data. However, these models do not describe well the fMRI signal when it is generated by multiple cognitive processes that are simultaneously active. In this paper we consider the problem of accurately modeling the fMRI signal of a human subject who is performing a task involving mul...