[PyMCon] An Introduction to Multi-Output Gaussian Processes Using PyMC

PyMCon Web Series: An Introduction to Multi-Output Gaussian Processes Using PyMC

Feb 21st, 22:00 UTC
Feb 21st, 5pm New York
Feb 21st, 2pm Los Angeles
Feb 22nd, 9am Sydney
Feb 22nd, 7am Tokyo

Speaker: Danh Phan

Abstract of the talk
Multi-output Gaussian processes have recently gained strong attention from researchers and have become an active research topic in machine learning’s multi-task learning. The advantage of multi-output Gaussian processes is their capacity to simultaneously learn and infer many outputs which have a similar source of uncertainty from inputs.

This talk presents to audiences how to build multi-output Gaussian processes in PyMC. It first introduces the concept of Gaussian processes (GPs) and multi-output GPs and how they can address real problems in several domains. It then shows how to implement multi-output GPs models such as the intrinsic coregionalization model (ICM) and the linear model of coregionalization (LCM) in Python using PyMC with real-world datasets.

The talk aims to get users quickly up and performing GPs, especially multi-output GPs using PyMC. Several examples with time-series datasets are used to illustrate different GPs features. This presentation will allow users to leverage GPs to analyze their data effectively.

Full abstract, code, and further details on PyMC Discourse

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Speaker
Danh Phan
https://www.linkedin.com/in/danhpt/