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Scalable and Reliable Inference for Probabilistic Modeling
2021
Probabilistic modeling, as known as probabilistic machine learning, provides a principled framework for learning from data, with the key advantage of offering rigorous solutions for uncertainty quantification. In the era of big and complex data, there is an urgent need for new inference methods in probabilistic modeling to extract information from data effectively and efficiently. This thesis shows how to do theoretically-guaranteed scalable and reliable inference for modern machine learning.
doi:10.7298/d364-gz12
fatcat:lsixokv3ofgtnlg3qlf6qaiqci