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Theodore Papamarkou

Adjunct Professor


Short Bio

My research spans approximate inference. I am interested in constructing approximate stochastic algorithms for intractable problems and in characterizing the tractability of such algorithms using complexity theory.

Topical questions in approximate inference that interest me include learning with big data or with high-dimensional models, approximate uncertainty quantification for deep learning, the construction of doubly stochastic processes using the notion of random environment, and the taxonomy of probabilistic complexity classes in relation to the P and NP classes.



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