pyro-ppl/pyro

[FR] Online (streaming) VI support

Open

#2,882 opened on Jun 21, 2021

View on GitHub
 (3 comments) (0 reactions) (0 assignees)Python (8,211 stars) (981 forks)batch import
enhancementhelp wanted

Description

It would be nice to have native support for online VI (its main 3 flavors are listed here: https://arxiv.org/abs/1904.03920 ).

Currently, the only tool applicable to the online learning scenario would be the SMCFilter, but it often performs poorly in high-dimensional situations.

SMCFilter itself could also benefit from particle MCMC rejuvenation as discussed here: https://www.springer.com/gp/book/9783030478445 and implemented in the particles package: https://github.com/nchopin/particles

Contributor guide

Tech stack
pythonpytorch
Domain
machine learning
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
5
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
over 1 week
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
needs investigation
Prerequisites
variational inferencePyro internalsSMCFilter
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
15
Research direction
Study the referenced arxiv paper (1904.03920) for online VI methods. Examine the current SMCFilter implementation in Pyro's source code. Review the particles package (nchopin/particles) for particle MCMC rejuvenation. Engage with maintainers to define the specific API and scope of the feature.
[FR] Online (streaming) VI support · pyro-ppl/pyro#2882 | Good First Issue