ML Anthology

Probabilistic Machine Learning: Models, Algorithms and a Programming Library

Jun Zhu
IJCAI 2018 pp. 5754-5759
doi:10.24963/IJCAI.2018/823 /ijcai/2018/zhu2018ijcai-probabilistic/

Abstract

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.

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Cite

Text

Zhu. "Probabilistic Machine Learning: Models, Algorithms and a Programming Library." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/823

Markdown

[Zhu. "Probabilistic Machine Learning: Models, Algorithms and a Programming Library." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/zhu2018ijcai-probabilistic/) doi:10.24963/IJCAI.2018/823

BibTeX

@inproceedings{zhu2018ijcai-probabilistic,
  title     = {{Probabilistic Machine Learning: Models, Algorithms and a Programming Library}},
  author    = {Zhu, Jun},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {5754-5759},
  doi       = {10.24963/IJCAI.2018/823},
  url       = {https://mlanthology.org/ijcai/2018/zhu2018ijcai-probabilistic/}
}