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Author ORCID Identifier


Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded


Month Degree Awarded


First Advisor

Justin Domke

Subject Categories

Applied Statistics | Artificial Intelligence and Robotics | Other Applied Mathematics | Other Mathematics | Probability | Statistical Methodology


Variational Inference performance strongly depends on many algorithmic choices, such as the optimization algorithm used, the objective, and the variational family. While the right combination for these components is highly problem dependent, there is currently little guidance on how to find it. This limits the use of these methods by non-expert users. This thesis advances Variational Inference towards an automatic and robust technology. We provide theoretically justified guiding principles for these decisions, and algorithms able to make optimal choices adaptively, creating Variational Inference based tools that are robust, flexible, and widely accessible.


Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Available for download on Thursday, August 01, 2024