Adding a state of the art Graph OoD Generalization paper #35
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
|
Thanks, Yongqiang! |
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Outdated suggestions cannot be applied.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.
Dear Dr. Guo,
Thank you very much for maintaining this amazing causality algorithm list!
We recently published a paper about learning causal representations on graphs in NeurIPS'22, which has obtained state-of-the-art performance on 30 datasets with various graph distribution shifts (still keep updating, more details are provided in our code repo). The first version of the work was released earlier as in arXiv 2202.05441. We think it would be a good fit for the list and would appreciate it if you could add our paper to this awesome list! 😆😆
Best regards
Yongqiang (Andrew) Chen