Scalable and Reliable Inference for Probabilistic Modeling

Ruqi Zhang
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.
more » ... nsidering both theory and practice, we provide foundational understanding of scalable and reliable inference methods and practical algorithms of new inference methods, as well as extensive empirical evaluation on common machine learning and deep learning tasks. Classical inference algorithms, such as Markov chain Monte Carlo, have enabled probabilistic modeling to achieve gold standard results on many machine learning tasks. However, these algorithms are rarely used in modern machine learning due
doi:10.7298/d364-gz12 fatcat:lsixokv3ofgtnlg3qlf6qaiqci