Probabilistic Machine Learning: Models, Algorithms and a Programming Library
Probabilistic Machine Learning: Models, Algorithms and a Programming Library
Jun Zhu
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Early Career. Pages 5754-5759.
https://doi.org/10.24963/ijcai.2018/823
Probabilistic machine learning provides a suite of powerful tools for modeling uncertainty, performing probabilistic inference, and making predictions or decisions in uncertain environments. In this paper, we present an overview of our recent work on probabilistic machine learning, including the theory of regularized Bayesian inference, Bayesian deep learning, scalable inference algorithms, a probabilistic programming library named ZhuSuan, and applications in representation learning as well as learning from crowds.
Keywords:
Machine Learning: Deep Learning
Machine Learning: Probabilistic Machine Learning
Machine Learning: Learning Generative Models
Machine Learning: Machine Learning