Efficient Methods for Unsupervised Learning of Probabilistic Models

05/19/2012

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In this thesis I develop a variety of techniques to train, evaluate, and sample from intractable and high dimensional probabilistic models. Abstract exceeds arXiv space limitations -- see PDF.

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Efficient Methods for Unsupervised Learning of Probabilistic Models

A Non-generative Framework and Convex Relaxations for Unsupervised Learning

The Probabilistic Model Checker Storm (Extended Abstract)

Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments

Generative Models Regression

Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)

A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems

High-dimensional Nonlinear Profile Monitoring based on Deep Probabilistic Autoencoders

Efficient Methods for Unsupervised Learning of Probabilistic Models

Related Research

A Non-generative Framework and Convex Relaxations for Unsupervised Learning

The Probabilistic Model Checker Storm (Extended Abstract)

Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments

Generative Models Regression

Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)

A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems

High-dimensional Nonlinear Profile Monitoring based on Deep Probabilistic Autoencoders