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Theodore Papamarkou

FR-Adjunct Professor


My research spans probabilistic machine learning. I am interested in addressing questions related to uncertainty quantification and to approximate inference with big data or with high-dimensional models. My two main research projects pertain to approximate Markov chain Monte Carlo (MCMC) for Bayesian deep learning and to neural network Gaussian processes (NNGPs).

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