Work

Probabilistic Machine Learning: Models, Algorithms and a Programming Library

Year: 2018

Type: conference-paper

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.

Source: International Joint Conference on Artificial Intelligence

DOI: 10.24963/ijcai.2018/823

Author: Jun Zhu

Institutions: Beijing National Research Center for Information Science and Technology, Tsinghua University

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: National Natural Science Foundation of China

Awards: G1919465925, G368795326