An Introduction to Multi-Output Gaussian Processes using PyMC
Speaker: Danh Phan
Event type: Live webinar
Date: Feb 21st 2023 (subscribe here for email updates)
Time: 22:00 UTC
Register for the event on Meetup to get the Zoom link
Talk Code Repository: On GitHub
Web App: https://danhphan.net/apps/interest-rate.html
NOTE: The event is recorded. Subscribe to the PyMC YouTube for notifications.
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Video: Interview with Danh Phan (7 minutes)
Video: Intro to Multi-Output Gaussian Processes Using PyMC
Welcome to the second event of the PyMCon Web Series! As part of this series, most events will have an async component and a live talk.
In this case, Danh, as part of the async component, prepared a full repository for the community to engage in before the talk. It includes multiple colabs, and pdf slide deck
Take a look before the talk to share your questions below and be prepared for the discussion and post
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.