Fix bug with Multitask DeepGP predictive variances. #2123
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
The latent variances of multitask DeepGP models are stored in non-interleaved covariance matrices. Previously, the MultitaskMultivariateNormal.marginal method implicitly assumed that the function covariance matrices were interleaved. In particular, this affects multitask DeepGP models which use a non-interleaved latent covariance matrix. With this PR, MultitaskMultivariateNormal.marginal now checks if the input covariance matrix is interleaved, and makes sure that the returned predictive covariance matrix matches the interleaved/non-interleaved pattern of the latent covariance matrix. [Fixes #2702]
Co-authored-by: Max Balandat <Balandat@users.noreply.github.com>
|
Closed #2072 (I accidentally linked the wrong issue number in the original commit!) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
The latent variances of multitask DeepGP models are stored in non-interleaved covariance matrices.
Previously, the MultitaskMultivariateNormal.marginal method implicitly assumed that the function
covariance matrices were interleaved.
In particular, this affects multitask DeepGP models which use a
non-interleaved latent covariance matrix.
With this PR, MultitaskMultivariateNormal.marginal now checks if the input covariance matrix is
interleaved, and makes sure that the returned predictive covariance matrix matches the
interleaved/non-interleaved pattern of the latent covariance matrix.
[Fixes #2702]